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50th EPS Conference on Plasma Physics, EPS 2024

作者： Valentini, A. Nocente, M. Eriksson, J. Järleblad, H. Moseev, D. Reman, B.C.G. Rud, M. Snicker, A. Stagner, L. Salewski, M. Department of Physics Technical University of Denmark Kgs Lyngby2800 Denmark Department of Physics University of Milano-Bicocca Milan20126 Italy Department of Physics and Astronomy Uppsala University Uppsala75120 Sweden Department of Appl. Math. & Computer Science Technical University of Denmark Kgs Lyngby2800 Denmark Max-Planck-Institut fur Plasmaphysik Wendelsteinstr. 1 Greifswald17491 Germany Laboratory for Plasma Physics LPP-ERM/KMS BrusselsB-1000 Belgium Department of Applied Physics Aalto University AaltoFI-00076 Finland General Atomics P.O. Box 85608 San DiegoCA92186-5609 United States

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Sampled-Data Control of Nonlinear Systems with Input Delay via Nonsmooth Feedback

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IFAC-PapersOnLine 2022年第36期55卷 270-275页

作者： Congran Zhao Wei Lin School of Math. Science Jiangsu University Zhenjiang China Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland Ohio 44106 USA

This paper studies sampled-data control of a class of nonlinear systems with input delay by memoryless feedback. A sampled-data nonsmooth feedback controller is first developed based on the emulation method and the adding a power integrator (AAPI) technique Lin and Qian [2000], Qian and Lin [2001]. With the aid of Lyapunov-Krasovskii functional theorem, together with the robust control design, we then prove that the proposed memoryless sampled-data controller renders the hybrid closed-loop systems with delay globally asymptotically stable, if the input delay and sampling period are limited. The family of uncertain systems under consideration goes beyond the global Lipschitz or linear growth condition and is genuinely nonlinear in the sense that it contains uncontrollable unstable linearization and is not smoothly stabilizable, even locally. Application of the nonsmooth sampled-data control scheme is illustrated by a simplified under-actuate mechanical system with input delay.

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RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models

arXiv

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arXiv 2024年

作者： Nath, Pritthijit Moss, Henry Shuckburgh, Emily Webb, Mark Department of Applied Math and Theoretical Physics University of Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge United Kingdom Met Office Hadley Centre United Kingdom

This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex math.matical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. Code accessible at https://***/p3jitnath/climate-rl. © 2024, CC BY-NC-SA.

关键词： Digital elevation model

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Fed-Haul: A Federated Learning Dual Band Point Multi-Point Backhaul Requirements in 5G Evolution and Beyond 23

Fed-Haul: A Federated Learning Dual Band Point Multi-Point B...

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6th International Conference on Networking, Intelligent Systems and Security, NISS 2023

作者： Chehri, Abdellah Chaibi, Hasna Mettiti, Abderrahmane Saadane, Rachid Jeon, Gwanggil Dep. of Math. and Computer Science Royal Military College of Canada Kingston Canada Supmti Genius Laboratory Hassan Rabat Morocco It Rabat Center Faculty of Sciences Mohammed v University Rabat Morocco SIRC-LAGES-EHTP Hassania School of Public Works Casalanca Morocco Department of Embedded Systems Engineering Incheon National University Korea Republic of

ISBN: (纸本)9798400700194

The rapid adoption of smartphones and the explosive growth of data traffic due to these devices have been phenomenal. As the world anticipates more connected devices - the Internet of Things (IoT), vehicle-to-vehicle (V2V) communications, and wearable devices - and more value-added applications and services (ultra-high-definition video, 360° video, virtual reality, smart cars, etc.), leading industry experts are calling for the sixth generation (6G) networks. Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. Federated learning (FL) is essential in optimizing wireless communication networks' resources. On the other hand, wireless communications are crucial for FL. Therefore, the purpose of this survey paper is to bridge this gap in the literature by discussing the interdependency between FL and backhaul wireless communications. © 2023 ACM.

关键词： 5G mobile communication systems

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A theory of link prediction via relational weisfeiler-leman on knowledge graphs 23

A theory of link prediction via relational weisfeiler-leman ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xingyue Huang Miguel Romero İsmail İlkan Ceylan Pablo Barceló Department of Computer Science University of Oxford Oxford UK Department of Computer Science Universidad Católica de Chile & CENIA Chile Inst. for Math. and Comp. Eng. Universidad Católica de Chile & IMFD Chile & CENIA Chile

Graph neural networks are prominent models for representation learning over graph-structured data. While the capabilities and limitations of these models are well-understood for simple graphs, our understanding remains incomplete in the context of knowledge graphs. Our goal is to provide a systematic understanding of the landscape of graph neural networks for knowledge graphs pertaining to the prominent task of link prediction. Our analysis entails a unifying perspective on seemingly unrelated models and unlocks a series of other models. The expressive power of various models is characterized via a corresponding relational Weisfeiler-Leman algorithm. This analysis is extended to provide a precise logical characterization of the class of functions captured by a class of graph neural networks. The theoretical findings presented in this paper explain the benefits of some widely employed practical design choices, which are validated empirically.

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Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis

arXiv

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arXiv 2024年

作者： Yang, Yifan Ban, Hao Huang, Minhui Ma, Shiqian Ji, Kaiyi Department of Computer Science and Engineering University at Buffalo United States AI Research for Monetization Meta United States Department of Computational Applied Math and Operations Research Rice University United States

Bilevel optimization has recently attracted considerable attention due to its abundant applications in machine learning problems. However, existing methods rely on prior knowledge of problem parameters to determine stepsizes, resulting in significant effort in tuning stepsizes when these parameters are unknown. In this paper, we propose two novel tuning-free algorithms, D-TFBO and S-TFBO. D-TFBO employs a double-loop structure with stepsizes adaptively adjusted by the "inverse of cumulative gradient norms" strategy. S-TFBO features a simpler fully single-loop structure that updates three variables simultaneously with a theory-motivated joint design of adaptive stepsizes for all variables. We provide a comprehensive convergence analysis for both algorithms and show that D-TFBO and S-TFBO respectively require (Equation presented) and (Equation presented) iterations to find an ϵ-accurate stationary point, (nearly) matching their well-tuned counterparts using the information of problem parameters. Experiments on various problems show that our methods achieve performance comparable to existing well-tuned approaches, while being more robust to the selection of initial stepsizes. To the best of our knowledge, our methods are the first to completely eliminate the need for stepsize tuning, while achieving theoretical guarantees. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Threshold Decision-Making Dynamics Adaptive to Physical Constraints and Changing Environment

Threshold Decision-Making Dynamics Adaptive to Physical Cons...

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European Control Conference (ECC)

作者： Giovanna Amorim María Santos Shinkyu Park Alessio Franci Naomi Ehrich Leonard Dept. of Mechanical and Aerospace Engineering Princeton Univ. Princeton NJ USA Computer Electrical and Math. Science and Eng. Div. King Abdullah Univ. of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Electrical Engineering and Computer Science University of Liege Liége Belgium WEL Research Institute Wavre Belgium

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We propose a threshold decision-making frame-work for controlling the physical dynamics of an agent switching between two spatial tasks. Our framework couples a nonlinear opinion dynamics model that represents the evolution of an agent's preference for a particular task with the physical dynamics of the agent. We prove the bifurcation that governs the behavior of the coupled dynamics. We show by means of the bifurcation behavior how the coupled dynamics are adaptive to the physical constraints of the agent. We also show how the bifurcation can be modulated to allow the agent to switch tasks based on thresholds adaptive to environmental conditions. We illustrate the benefits of the approach through a multi-robot task allocation application for trash collection.

关键词： Adaptation models Decision making Europe Switches Bifurcation Control systems Resource management

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A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs

arXiv

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arXiv 2023年

作者： Cucumides, Tamara Daza, Daniel Barceló, Pablo Cochez, Michael Geerts, Floris Reutter, Juan L. Romero, Miguel Department of Computer Science University of Antwerp Belgium Computer Science Vrije Universiteit Amsterdam Netherlands Discovery Lab Elsevier Amsterdam Netherlands Inst. for Math. and Comp. Eng. Universidad Católica de Chile IMFD Chile CENIA Chile Chile Department of Computer Science Universidad Católica de Chile Chile

The challenge of answering graph queries over incomplete knowledge graphs is gaining significant attention in the machine learning community. Neuro-symbolic models have emerged as a promising approach, combining good performance with high interpretability. These models utilize trained architectures to execute atomic queries and integrate modules that mimic symbolic query operators. However, most neuro-symbolic query processors are constrained to tree-like graph pattern queries. These queries admit a bottom-up execution with constant values or anchors at the leaves and the target variable at the root. While expressive, tree-like queries fail to capture critical properties in knowledge graphs, such as the existence of multiple edges between entities or the presence of triangles. We introduce a framework for answering arbitrary graph pattern queries over incomplete knowledge graphs, encompassing both cyclic queries and tree-like queries with existentially quantified leaves. These classes of queries are vital for practical applications but are beyond the scope of most current neuro-symbolic models. Our approach employs an approximation scheme that facilitates acyclic traversals for cyclic patterns, thereby embedding additional symbolic bias into the query execution process. Our experimental evaluation demonstrates that our framework performs competitively on three datasets, effectively handling cyclic queries through our approximation strategy. Additionally, it maintains the performance of existing neuro-symbolic models on anchored tree-like queries and extends their capabilities to queries with existentially quantified variables. © 2023, CC BY.

关键词： Trees (math.matics)

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj Computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs

arXiv

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arXiv 2023年

作者： Huang, Xingyue Ceylan, İsmail İlkan Orth, Miguel Romero Barceló, Pablo Department of Computer Science University of Oxford Oxford United Kingdom Department of Computer Science Universidad Católica de Chile CENIA Chile Chile Inst. for Math. and Comp. Eng. Universidad Católica de Chile IMFD Chile CENIA Chile Chile

关键词： Knowledge graph

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