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Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

research 2022年第4期2022卷 137-146页

作者： Xiong Ying Si-Yang Leng Huan-Fei MaD Qing Nie Ying-Cheng Lai Wei Lin School of Mathematical Sciences SCMSand SCAMFudan UniversityShanghai 200433China Research Institute for Inteligent Complex Systems CCSBand LCNBIFudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain ScienceFudan UniversityShanghai 200032China Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mathematical Sciences Soochow UniversitySuzhou 215006China Department of Mathematics Department of Developmental and Cll BiologyAnd NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCA 92697-3875USA School of Electrical ComputerAnd Energy EngineeringArizona State UniversityTempeArizona 85287-5706USA Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Data-based detection and quantification of causation in complex,nonlinear dynamical systems is of paramount importance to science,engineering,and *** by the widely used methodology in recent years,the cross-map-based techniques,we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms,which is consistent with the natural interpretation of *** particular,instead of measuring the smoothness of the cross-map as conventionally implemented,we define causation through measuring the scaling law for the continuity of the investigated dynamical system *** uncovered scaling law enables accurate,reliable,and eficient detection of causation and assessment of its strength in general complex dynamical systems,outperforming those existing representative *** continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

关键词： dynamical scaling continuity

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A Fuzzy Logic-Based Computational Framework for Precision Triage in Androgenetic Alopecia: A Simulated Biomarker-Driven Approach

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International Journal of Computational Intelligence systems 2025年第1期18卷 1-30页

作者： Al-Samarraay, Mohammed S. Magableh, Aws A. Mahmood, Rana I. Joudar, Shahad Sabbar Zahid, Idrees A. Al-Obaidi, Jameel R. Al-Saffar, Ali Z. Albahri, A. S. Albahri, O. S. Alamoodi, A. H. Abdullah, Mohd Faizal Nizam Lee Electrical and Electronic Engineering Department College of Engineering Gulf University Sanad Bahrain Faculty of Information Technology and Computer Sciences Department of Information Systems Yarmouk University Irbid Jordan Department of Software Engineering Prince Sultan University Riyadh Saudi Arabia Department of Biomedical Engineering College of Engineering Al-Nahrain University Jadriya Baghdad Iraq University of Technology Baghdad Iraq Department of Biology Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Department of Molecular and Medical Biotechnology College of Biotechnology Al-Nahrain University Baghdad Iraq Technical Engineering College Imam Ja’afar Al-Sadiq Baghdad Iraq University of Information Technology and Communications (UOITC) Baghdad Iraq Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Victorian Institute of Technology Melbourne Australia GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University for Science and Technology Mishref Kuwait Department of Mathematics Faculty of Science and Mathematics Universiti Pendidikan Sultan Idris Tanjong Malim Malaysia

Androgenetic alopecia (AGA) is a common cause of hair loss affecting both men and women. Although its precise etiology remains uncertain, genetic and hormonal factors are recognized as major contributors. This study introduces a computational intelligence framework employing fuzzy logic and multi-criteria decision-making (MCDM) to simulate a robust triage system for AGA management. Using a simulated dataset of 100 AGA patients, we applied the fuzzy-weighted zero-inconsistency (FWZIC) method to assign weights to 11 bioactive criteria associated with AGA. These weights informed a novel triage procedure for alopecia patients (TPAP), which stratified patients into seven severity levels (level 1: minor; level 7: severe). This study presents a computationally intelligent triage model tailored for AGA, emphasizing the applicability of fuzzy MCDM techniques in medical decision support. The TPAP framework can assist in resource allocation and treatment planning, paving the way for personalized and timely interventions in hair loss management.

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Overcoming common flaws in the evaluation of selective classification systems 24

Overcoming common flaws in the evaluation of selective class...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Jeremias Traub Till J. Bungert Carsten T. Lüth Michael Baumgartner Klaus H. Maier-Hein Lena Maier-Hein Paul F. Jäger German Cancer Research Center (DKFZ) Heidelberg Interactive Machine Learning Group Germany and Helmholtz Imaging DKFZ Heidelberg Germany German Cancer Research Center (DKFZ) Heidelberg Interactive Machine Learning Group Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Medical Image Computing (MIC) Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Medical Image Computing (MIC) Germany and Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg Helmholtz Imaging DKFZ Heidelberg Germany and DKFZ Heidelberg Division of Intelligent Medical Systems (IMSY) Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg

ISBN: (纸本)9798331314385

Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these systems typically assumes fixed working points based on pre-defined rejection thresholds, methodological progress requires benchmarking the general performance of systems akin to the AUROC in standard classification. In this work, we define 5 requirements for multi-threshold metrics in selective classification regarding task alignment, interpretability, and flexibility, and show how current approaches fail to meet them. We propose the Area under the Generalized Risk Coverage curve (AUGRC), which meets all requirements and can be directly interpreted as the average risk of undetected failures. We empirically demonstrate the relevance of AUGRC on a comprehensive benchmark spanning 6 data sets and 13 confidence scoring functions. We find that the proposed metric substantially changes metric rankings on 5 out of the 6 data sets.

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Quantum Technologies for Beyond 5G and 6G Networks: Applications, Opportunities, and Challenges

arXiv

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arXiv 2025年

作者： Zeydan, Engin De Alwis, Chamitha Khan, Rabia Turk, Yekta Aydeger, Abdullah Gadekallu, Thippa Reddy Liyanage, Madhusanka Barcelona08860 Spain School of Computer Science and technology University of Bedfordshire United Kingdom Transmission Perth United Kingdom Aselsan Corp. Istanbul34380 Turkey Department of Electrical Engineering and Computer Science Florida Institute of Technology United States College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India School of Computer Science University College Dublin Ireland

As the world prepares for the advent of 6G networks, quantum technologies are becoming critical enablers of the next generation of communication systems. This survey paper investigates the convergence of quantum technologies and 6G networks, focusing on their applications, opportunities and challenges. We begin with an examination of the motivations for integrating quantum technologies into 6G, investigating the potential to overcome the limits of classical computing and cryptography. We then highlight key research gaps, particularly in quantum communication, quantum computing integration and security enhancement. A comprehensive overview of quantum technologies relevant to 6G, including quantum communication devices, quantum computing paradigms, and hybrid quantum-classical approaches is provided. A particular focus is on the role of quantum technologies in enhancing 6G Radio Access Networks (RAN), 6G core and edge network optimization, and 6G security. The survey paper also explores the application of quantum cryptography with a focus on Quantum Key Distribution (QKD), Quantum Secure Direct Communication (QSDC) and quantum-resistant cryptographic algorithms and assesses their implementation challenges and potential impact on 6G networks. We also discuss the significant challenges associated with integrating quantum technologies into existing communications infrastructures, including issues of technological maturity, standardization, and economic considerations. Finally, we summarize the lessons learned from current research and outline future research directions to guide the ongoing development of quantum-enabled 6G networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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On the link between explicit MPC and the face lattice of the lifted feasible domain

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IFAC-PapersOnLine 2022年第16期55卷 308-313页

作者： Ştefan S. Mihai Florin Stoican Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University "Politehnica" of Bucharest 313 Splaiul Independenţei Sector 6 060042 Bucharest Romania Research Center for Logic Optimization and Security (LOS) Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest.

The paper provides a geometric interpretation for the explicit solution of the quadratic cost, linear-constrained MPC (model predictive control) problem. We link the face lattice of the lifted feasible domain (defined in the input and parameter space) with the critical regions which partition the parameter space and serve as polyhedral support for the piecewise affine explicit MPC solution. We provide geometric (face visibility) and algebraic (polyhedron emptiness) tests for the pruning of the candidate sets of active constraints.

关键词： Model Predictive Control (MPC) Explicit MPC Face Lattice Constrained-Optimization Problem

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

arXiv

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China 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

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multi-player non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

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Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis

arXiv

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arXiv 2023年

作者： Dong, Yanjie Wang, Luya Chi, Yuanfang Wang, Jia Zhang, Haijun Yu, Fei Richard Leung, Victor C.M. Hu, Xiping The Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China The College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China The Department of Systems and Computer Engineering Carleton University Ottawa Canada The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks. © 2023, CC BY.

关键词： Momentum

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Thermal resistance prediction model for IC packaging optimization and design cycle reduction

Thermal resistance prediction model for IC packaging optimiz...

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Electronic Components and Technology Conference (ECTC)

作者： Guan-Wei Chen Yan-Cheng Lin Chung-Hsiang Hsu Tang-Yuan Chen Chen-Chao Wang Chin-Pin Hung Hung-Kai Wang Institute of Innovative Semiconductor and Sustainable Manufacturing Nantinal Cheng – Kung University Tainan Taiwan Institute of Manufacturing Inforamtion and Systems Nantinal Cheng – Kung University Tainan Taiwan Department of Manufacturing Inforamtion and Systems Nantinal Cheng – Kung University Tainan Taiwan Group Corporate Research and Development Division Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan Corporate R&D Center Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan Advanced Semiconductor Engineering Inc. Kaohsiung Taiwan Department of Computer Science and Information Engineering Nantinal Cheng - Kung University Tainan Taiwan

ISBN: (数字)9798350375985

ISBN: (纸本)9798350375992

The development of highly reliable packaging products is paramount, as accurately predicting the thermal resistance values (Theta JA, JB, JC) of IC packages is crucial for calculating maximum power dissipation and mitigating self-heating effects. In this paper, we focus on the prediction and analysis of thermal resistance in packaged products. Traditionally, finite element methods (FEM) are used for simulations to predict thermal resistance. However, these simulations often employ idealized assumptions that may not align with real-world conditions. Furthermore, traditional simulation methods require substantial memory space and high-performance hardware, leading to significantly increased computation times as the complexity of the simulation increases, thereby extending the product verification timeline. Machine learning methods can effectively improve operational speed and reduce costs. This research utilizes datasets generated from past FEM simulations, testing various machine learning models, among which the Artificial Neural Network (ANN) models, a deep learning approach, yielded the best results. These models were then primarily employed for predicting the characteristics of packaged components. The findings indicate that the predicted results closely align with the FEM simulation data, with the percentage error in thermal resistance for QFN packages being less than 5%, thereby achieving structural optimization of packaged products and shortening the design cycle. Additionally, a user interface platform was developed to enhance research efficiency significantly. researchers simply need to select the desired packaging technology product and enter the structural parameters to generate predictive results and visual charts, making it accessible even for operators lacking programming skills. Ultimately, the goal is to achieve visual structural optimization and shorten the design cycle for IC packaged products, serving as a basis for future structural studies of prod

关键词： Visualization Thermal resistance Computational modeling Artificial neural networks Predictive models Packaging Electronic packaging thermal management

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Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification 16th

Multi-feature Fusion Based Deep Forest for Hyperspectral Ima...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Liu, Xiaobo Mohammad, Mostofa Zaman Zhang, Chaochao Gong, Xin Cai, Zhihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789811912528

Multi-feature fusion is a useful way to improve the classification of hyperspectral image (HSI). But the multi-feature fusion is usually at the decision level of classifier, which causes less link between features or poor extensibility of feature. In this paper, we propose a multi-feature fusion based deep forest method for HSI classification, named mfdForest. In mfdForest, the morphological features, saliency features, and edge features are extracted, then the three deep multi-grained scanning branches in dgcForest (one of improved deep forest) are used to extract and fuse the extracted features deeply, and the fused features are sent into cascade forest in dgcForest for classification. Experimental results indicate that the proposed framework consumes less training time and has better performance on two HSI data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Spectroscopy

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Robust Co-Design of Canonical Underactuated systems for Increased Certifiable Stability

Robust Co-Design of Canonical Underactuated Systems for Incr...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Federico Girlanda Lasse Shala Shivesh Kumar Frank Kirchner Robotics Innovation Center German Research Center for Artificial Intelligence (DFKI GmbH) Bremen Germany Department of Information Engineering University of Padova Italy Department of Mechanics & Maritime Sciences Dynamics Division Chalmers University of Technology Gotheburg Sweden Faculty of Mathematics and Computer Science University of Bremen Bremen Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Optimal behaviours of a system to perform a specific task can be achieved by leveraging the coupling between trajectory optimization, stabilization, and design optimization. This approach is particularly advantageous for underactuated systems, which are systems that have fewer actuators than degrees of freedom and thus require for more elaborate control systems. This paper proposes a novel co-design algorithm, namely Robust Trajectory Control with Design optimization (RTC-D). An inner optimization layer (RTC) simultaneously performs direct transcription (DIRTRAN) to find a nominal trajectory while computing optimal hyperparameters for a stabilizing time-varying linear quadratic regulator (TVLQR). RTC-D augments RTC with a design optimization layer, maximizing the system’s robustness through a time-varying Lyapunov-based region of attraction (ROA) analysis. This analysis provides a formal guarantee of stability for a set of off-nominal states. The proposed algorithm has been tested on two different underactuated systems: the torque-limited simple pendulum and the cart-pole. Extensive simulations of off-nominal initial conditions demonstrate improved robustness, while real-system experiments show increased insensitivity to torque disturbances.

关键词： Couplings Torque Regulators Stability analysis Robustness Task analysis Robotics and automation

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