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SSRN 2024年

作者： Hu, Jia Lei, Mingyue Li, Duo Li, Zhenning So, Jaehyun Wang, Haoran Key Laboratory of Road and Traffic Engineering Ministry of Education Institute for Advanced Study Tongji University 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University 4800 Cao’an Road Shanghai China Department of Civil and Geospatial Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau Avenida da Universidade Taipa China Department of Transportation Systems Engineering Ajou University 206 World cup-ro Yeongtong-gu Gyeonggi-do Suwon-si16499 Korea Republic of State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China

Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot computing power and cannot be applied online. To address this challenge, this paper proposes a lesson learning approach: learning from driver's takeover interventions. By leveraging online takeover data, the driving zone is generated to ensure perceived safety using Gaussian discriminant analysis. Real-time corrections to trajectory planning rewards are enacted through apprenticeship learning. Guided by the objective of optimizing rewards within the constraints of the driving zone, this approach employs model predictive control for trajectory planning. This lesson learning framework is highlighted for its faster evolution capability, adeptness at experience accumulating, assurance of perceived safety, and computational efficiency. Simulation results demonstrate that the proposed system consistently achieves a successful customization without further takeover interventions. Accumulated experience yields a 24% enhancement in evolution efficiency. The average number of learning iterations is only 13.8. The average computation time is 0.08 seconds. © 2024, The Authors. All rights reserved.

关键词： Discriminant analysis

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Online Phase Reconstruction via DNN-based Phase Differences Estimation

arXiv

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arXiv 2022年

作者： Masuyama, Yoshiki Yatabe, Kohei Nagatomo, Kento Oikawa, Yasuhiro The Department of Computer Science Graduate School of Systems Design Tokyo Metropolitan University Hino Tokyo191-0065 Japan The Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo184-8588 Japan The Department of Intermedia Art and Science Waseda University Tokyo169-8555 Japan

This paper presents a two-stage online phase reconstruction framework using causal deep neural networks (DNNs). Phase reconstruction is a task of recovering phase of the short-time Fourier transform (STFT) coefficients only from the corresponding magnitude. However, phase is sensitive to waveform shifts and not easy to estimate from the magnitude even with a DNN. To overcome this problem, we propose to use DNNs for estimating differences of phase between adjacent time-frequency bins. We show that convolutional neural networks are suitable for phase difference estimation, according to the theoretical relation between partial derivatives of STFT phase and magnitude. The estimated phase differences are used for reconstructing phase by solving a weighted least squares problem in a frame-by-frame manner. In contrast to existing DNN-based phase reconstruction methods, the proposed framework is causal and does not require any iterative procedure. The experiments showed that the proposed method outperforms existing online methods and a DNN-based method for phase reconstruction. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Acid–base Homeostasis and Implications to the Phenotypic Behaviors of Cancer

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Genomics, Proteomics & Bioinformatics 2023年第6期21卷 1133-1148页

作者： Yi Zhou Wennan Chang Xiaoyu Lu Jin Wang Chi Zhang Ying Xu Cancer Systems Biology Center China–Japan Union HospitalJilin UniversityChangchun 130033China Department of Biochemistry and Molecular Biology and Institute of Bioinformatics University of GeorgiaAthensGA 30602USA Department of Medical and Molecular Genetics Indiana University School of MedicineIndianapolisIN 46202USA Department of Electrical and Computer Engineering Purdue UniversityWest LafayetteIN 47907USA Department of Biohealth Informatics Indiana University–Purdue University IndianapolisIndianapolisIN 46202USA Departments of Chemistry and of Physics and Astronomy Stony Brook UniversityStony BrookNY 11794USA

Acid–base homeostasis is a fundamental property of living cells,and its persistent disruption in human cells can lead to a wide range of *** this study,we conducted a computational modeling analysis of transcriptomic data of 4750 human tissue samples of 9 cancer types in The Cancer Genome Atlas(TCGA)*** on our previous study,we quantitatively estimated the average production rate of OHby cytosolic Fenton reactions,which continuously disrupt the intracellular pH(pHi)*** predictions indicate that all or at least a subset of 43 reprogrammed metabolisms(RMs)are induced to produce net protons(H+)at comparable rates of Fenton reactions to keep the pHi *** then discovered that a number of wellknown phenotypes of cancers,including increased growth rate,metastasis rate,and local immune cell composition,can be naturally explained in terms of the Fenton reaction level and the induced *** study strongly suggests the possibility to have a unified framework for studies of cancerinducing stressors,adaptive metabolic reprogramming,and cancerous *** addition,strong evidence is provided to demonstrate that a popular view that Na+/H+exchangers along with lactic acid exporters and carbonic anhydrases are responsible for the intracellular alkalization and extracellular acidification in cancer may not be justified.

关键词： Acid-basehomeostasis Cancer microenvironment Metabolic reprogramming Fenton reaction Ironmetabolism

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Reliability-informed life-cycle warranty cost analysis: A case study on a transmission in agricultural equipment

Reliability-informed life-cycle warranty cost analysis: A ca...

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ASME 2020 International design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2020

作者： Li, Meng Liu, Jinqiang Nemani, Venkat Pavan Ahmed, Navaid Kremer, Gül E. Hu, Chao Department of Mechanical Engineering Iowa State University AmesIA United States Department of Electrical and Computer Engineering Iowa State University AmesIA United States John Deere Reman Deere and Company SpringfieldMO65803 United States Department of Industrial and Manufacturing Systems Engineering Iowa State University AmesIA50011 United States Departments of Mechanical Engineering and Electrical and Computer Engineering Iowa State University AmesIA United States

ISBN: (纸本)9780791883952

In agricultural and industrial equipment, both new and remanufactured systems are often available for warranty coverage. In such cases, it may be challenging for equipment manufacturers to properly trade-off between the system reliability and the cost associated with a replacement option (e.g., replace with a new or remanufactured system). To address this problem, we present a reliability-informed life-cycle warranty cost (LCWC) analysis framework that enables equipment manufacturers to evaluate different warranty policies. These warranty policies differ in whether a new or remanufactured system is used for replacement in the case of product failure. The novelty of this LCWC analysis framework lies in its ability to incorporate real-world field reliability data into warranty policy assessment using probabilistic warranty cost models that consider multiple life cycles. First, the reliability functions for the new and remanufactured systems are built as the time-to-failure distributions that provide the best-fit to the field reliability data. Then, these reliability functions and their corresponding warranty policies are used to build the LCWC models according to the specific warranty terms. Finally, Monte Carlo simulation is used to propagate the time-to-failure uncertainty of each system, modeled by its reliability function, through each LCWC model to produce a probability distribution of the LCWC. The effectiveness of the proposed reliabilityinformed LCWC analysis framework is demonstrated with a real-world case study on a transmission used in some agricultural equipment. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Cost benefit analysis

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Unique Ergodicity in the Interconnections of Ensembles with Applications to Two-Sided Markets

arXiv

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arXiv 2021年

作者： Griggs, Wynita M. Ghosh, Ramen Mareček, Jakub Shorten, Robert N. The Department of Civil Engineering The Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia The School of Electrical and Electronic Engineering University College Dublin Ireland The Czech Technical Univeristy in Prague Czech Republic The Dyson School of Design Engineering Imperial College London South Kingston United Kingdom

There has been much recent interest in two-sided markets and dynamics thereof. In a rather a general discrete-time feedback model, which we show conditions that assure that for each agent, there exists the limit of a long-run average allocation of a resource to the agent, which is independent of any initial conditions. We call this property the unique ergodicity. Our model encompasses two-sided markets and more complicated interconnections of workers and customers, such as in a supply chain. It allows for non-linearity of the response functions of market participants. Finally, it allows for uncertainty in the response of market participants by considering a set of the possible responses to either price or other signals and a measure to sample from these. Copyright © 2021, The Authors. All rights reserved.

关键词： Supply chains

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LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach

arXiv

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arXiv 2025年

作者： Liu, Yinqiu Liu, Guangyuan Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Sun, Geng Xiong, Zehui Han, Zhu College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering University of Houston Houston United States Department of Computer Science and Engineering Kyung Hee University Korea Republic of

Nowadays, Generative AI (GenAI) reshapes numerous domains by enabling machines to create content across modalities. As GenAI evolves into autonomous agents capable of reasoning, collaboration, and interaction, they are increasingly deployed on network infrastructures to serve humans automatically. This emerging paradigm, known as the agentic network, presents new optimization challenges due to the demand to incorporate subjective intents of human users expressed in natural language. Traditional generic Deep Reinforcement Learning (DRL) struggles to capture intent semantics and adjust policies dynamically, thus leading to suboptimality. In this paper, we present LAMeTA, a Large AI Model (LAM)-empowered Two-stage Approach for intent-aware agentic network optimization. First, we propose Intent-oriented Knowledge Distillation (IoKD), which efficiently distills intent-understanding capabilities from resource-intensive LAMs to lightweight edge LAMs (E-LAMs) to serve end users. Second, we develop Symbiotic Reinforcement Learning (SRL), integrating E-LAMs with a policy-based DRL framework. In SRL, E-LAMs translate natural language user intents into structured preference vectors that guide both state representation and reward design. The DRL, in turn, optimizes the generative service function chain composition and E-LAM selection based on real-time network conditions, thus optimizing the subjective Quality-of-Experience (QoE). Extensive experiments conducted in an agentic network with 81 agents demonstrate that IoKD reduces mean squared error in intent prediction by up to 22.5%, while SRL outperforms conventional generic DRL by up to 23.5% in maximizing intent-aware QoE. © 2025, CC BY.

关键词： Deep reinforcement learning

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Gemnet: Group Equivariant Mesh Convolutional Neural Network

SSRN

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SSRN 2022年

作者： Afzal, Muhammad Kamran Zang, Yu Zeb, Adnan Afzal, H.M. Rehan Liu, Weiquan Wang, Cheng Li, Jonathan Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University 422 Siming South Road Xiamen361005 China College of Engineering Southern University of Science and Technology Shenzhen China School of Electrical Engineering and Computing University of Newcastle CallaghanNSW2308 Australia Departments of Geography and Environmental Management and Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Recent convolutional neural networks (CNNs) in geometric deep-learning for 3D meshes are inadequate in a natural generalisation of CNNs that reduces sample complexity by exploiting symmetries. We propose a novel Group Equivariant Mesh Convolutional Neural Network (GEMnet) using group convolutional layers that exploit a higher degree of weight sharing than the traditional convolution layers, enhancing the expressive ability of the network without fostering additional parameters. Implementing GEMnet layers is easy, with nominal computational overhead for discrete groups produced by translations, rotations, and reflections. Our novel approach operates directly on 3D mesh data for more discriminant feature maps, exhibiting notable improvements across prior stateof-the-art methods. Employing a variety of benchmark datasets, i.e., SHREC with Split 16 & 10 reached 99.83% & 99.33%, Cubes gained 99.17%, and ModelNet40 datasets achieved 95.50% accuracies, respectively, our method surpasses traditional methods for triangular meshes in a classification task. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming

arXiv

引用

arXiv 2022年

作者： Dvorkin, Vladimir Fioretto, Ferdinando Van Hentenryck, Pascal Pinson, Pierre Kazempour, Jalal Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48103 United States Department of Computer Science University of Virginia CharlottesvilleVA22903 United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States Dyson School of Design Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Wind and Energy Systems Technical University of Denmark Lyngby2800 Denmark

Convex optimization finds many real-life applications, where – optimized on real data – optimization results may expose private data attributes (e.g., individual health records, commercial information), thus leading to privacy breaches. To avoid these breaches and formally guarantee privacy to optimization data owners, we develop a new privacy-preserving perturbation strategy for convex optimization programs by combining stochastic (chance-constrained) programming and differential privacy. Unlike standard noise-additive strategies, which perturb either optimization data or optimization results, we express the optimization variables as functions of the random perturbation using linear decision rules;we then optimize these rules to accommodate the perturbation within the problem’s feasible region by enforcing chance constraints. This way, the perturbation is feasible and makes different, yet adjacent in the sense of a given distance function, optimization datasets statistically similar in randomized optimization results, thereby enabling probabilistic differential privacy guarantees. The chance-constrained optimization additionally internalizes the conditional value-at-risk measure to model the tolerance towards the worst-case realizations of the optimality loss w.r.t. the non-private solution. We demonstrate the privacy properties of our perturbation strategy analytically and through optimization and machine learning applications. © 2022, CC BY.

关键词： Convex optimization

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Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model

arXiv

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arXiv 2025年

作者： Liu, Chenyang Chen, Keyan Zhao, Rui Zou, Zhengxia Shi, Zhenwei The Department of Aerospace Intelligent Science and Technology School of Astronautics The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Recently, generative foundation models have significantly advanced large-scale text-driven natural image generation and have become a prominent research trend across various vertical domains. However, in the remote sensing field, there is still a lack of research on large-scale text-to-image (text2image) generation technology. Existing remote sensing image-text datasets are small in scale and confined to specific geographic areas and scene types. Besides, existing text2image methods have struggled to achieve global-scale, multi-resolution controllable, and unbounded image generation. To address these challenges, this paper presents two key contributions: the Git-10M dataset and the Text2Earth foundation model. Git-10M is a global-scale image-text dataset comprising 10.5 million image-text pairs, 5 times larger than the previous largest one. The dataset contains essential resolution information and covers a wide range of geographic scenes and contains essential geospatial metadata, significantly surpassing existing datasets in both size and diversity. Building on Git-10M, we propose Text2Earth, a 1.3 billion parameter generative foundation model based on the diffusion framework to model global-scale remote sensing scenes. Text2Earth integrates a resolution guidance mechanism, enabling users to specify image resolutions. A dynamic condition adaptation strategy is proposed for training and inference to improve image generation quality. Text2Earth not only excels in zero-shot text2image generation but also demonstrates robust generalization and flexibility across multiple tasks, including unbounded scene construction, image editing, and cross-modal image generation. This robust capability surpasses previous models restricted to the basic fixed size and limited scene types. On the previous text2image benchmark dataset, Text2Earth outperfoms previous models with a significant improvement of +26.23 FID and +20.95% Zero-shot Cls-OA metric. Our project page is https://chen-y

关键词： Optical remote sensing

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Wireless edge-empowered metaverse: A learning-based incentive mechanism for virtual reality

arXiv

引用

arXiv 2021年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Miao, Chunyan Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The Metaverse is regarded as the next-generation Internet paradigm that allows humans to play, work, and socialize in an alternative virtual world with immersive experience, for instance, via head-mounted display for Virtual Reality (VR) rendering. With the help of ubiquitous wireless connections and powerful edge computing technologies, VR users in the wireless edge-empowered Metaverse can immerse themselves in the virtual through the access of VR services offered by different providers. However, VR applications are computation- and communication-intensive. The VR service providers (SPs) have to optimize the VR service delivery efficiently and economically given their limited communication and computation resources. An incentive mechanism can be thus applied as an effective tool for managing VR services between providers and users. Therefore, in this paper, we propose a learning-based Incentive Mechanism framework for VR services in the Metaverse. First, we propose the quality of perception as the metric for VR users immersing in the virtual world. Second, for quick trading of VR services between VR users (i.e., buyers) and VR SPs (i.e., sellers), we design a double Dutch auction mechanism to determine optimal pricing and allocation rules in this market. Third, for auction information exchange cost reduction, we design a deep reinforcement learning-based auctioneer to accelerate this auction process. Experimental results demonstrate that the proposed framework can achieve near-optimal social welfare while reducing at least half of the auction information exchange cost than baseline methods. © 2021, CC BY.

关键词： Virtual reality

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