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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

A parallelized cellular Potts model that enables simulations at tissue scale

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arXiv 2023年

作者： Sultan, Shabaz Devi, Sapna Mueller, Scott N. Textor, Johannes Data Science group Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Department of Medical BioSciences Radboud University Medical Center Nijmegen Netherlands Department of Microbiology and Immunology The University of Melbourne The Peter Doherty Institute for Infection and Immunity MelbourneVIC Australia

The Cellular Potts Model (CPM) is a widely used simulation paradigm for systems of interacting cells that has been used to study scenarios ranging from plant development to morphogenesis, tumor growth and cell migration. Despite their wide use, CPM simulations are considered too computationally intensive for three-dimensional (3D) models at organ scale. CPMs have been difficult to parallelise because of their inherently sequential update scheme. Here, we present a Graphical Processing Unit (GPU)-based parallelisation scheme that preserves local update statistics and is up to 3-4 orders of magnitude faster than serial implementations. We show several examples where our scheme preserves simulation behaviors that are drastically altered by existing parallelisation methods. We use our framework to construct tissue-scale models of liver and lymph node environments containing millions of cells that are directly based on microscopy-imaged tissue structures. Thus, our GPU-based CPM framework enables in silico studies of multicellular systems of unprecedented scale. © 2023, CC BY.

关键词： Tissue

Optimal AoI-based Block Propagation and Incentive Mechanism for Blockchain Networks in Web 3.0

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Wen, Jinbo Kang, Jiawen Xiong, Zehui Du, Hongyang Yang, Zhaohui Niyato, Dusit Shen, Meng Jiao, Yutao Zhang, Yang Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Guangdong University of Technology School of Automation China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Hong Kong University Department of Electrical and Electronic Engineering Hong Kong Zhejiang University College of Information Science and Electronic Engineering China Nanyang Technological University College of Computing and Data Science Singapore Beijing Institute of Technology School of Cyberspace Science and Technology China Army Engineering University of PLA College of Communications Engineering China

Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, blockchain-based Web 3.0 is still in its infancy, such as ensuring block freshness and optimizing block propagation to improve blockchain performance. In this paper, we develop a freshness-aware block propagation optimization framework for Web 3.0. We first propose a novel metric called Age of Block information (AoBI) based on the concept of age of information to quantify block freshness. AoBI measures the time elapsed from the freshest transaction generation to the completion of block consensus. To make block propagation optimization tractable, we classify miners into five different states and propose a block propagation model for public blockchains inspired by epidemic models. Moreover, considering that the miners are bounded rational, we propose an incentive mechanism based on the evolutionary game for block propagation to improve block propagation efficiency. Numerical results demonstrate that compared with other block propagation mechanisms in public blockchains, the proposed scheme has a higher block forwarding probability, which improves block propagation efficiency and decreases the minimum value of average AoBI. © 2015 IEEE.

关键词： age of information block propagation evolutionary game Web 3.0 wireless networks

Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions.

关键词： Heating systems Analytical models Array signal processing Communication systems Atmospheric modeling System performance Modulation Rayleigh channels Optimization Signal to noise ratio

Innovative Urban Design Simulation: Utilizing Agent-Based Modelling through Reinforcement Learning 23

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Innovative Urban Design Simulation: Utilizing Agent-Based Mo...

Proceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems

作者： Ayse Glass Jorg R. Noennig Burak Bek Roman Glass Eylul K. Menges Iryna Okhrin Pramod Baddam Mariela Rossana Sanchez Gunalan Senthil René Jäkel Digital City Science Hafencity Universitat Hamburg Germany University Grenoble Alpes France Faculty of Architecture izmir Institute of Technology Turkiye ScaDS.AI - Center for Scalable Data Analytics and Artificial Intelligence Technical University of Dresden Germany and Center for Information Services and High-Performance Computing (ZIH)Technical University of Dresden Germany Center for Information Services and High-Performance Computing (ZIH) Technical University of Dresden Germany

ISBN: (纸本)9798400709067

data-driven design for cities is improving the quality of everyday life of citizens and optimizes the usage of resources. A new aspect is artificial intelligence, which Smart Cities could greatly benefit from. A central problem for urban designers is the unavailability of data to make relevant decisions. Agent-based simulations enable a view of the dynamic properties of the urban system, generating data in its course. However, the simulation must remain sufficiently simple to remain in the realm of computability. The research question of this paper is: How can we make agents behave more realistically to analyze citizens’ mobility behavior? To solve this problem, we first created a simulated virtual environment, where agents can move freely in a small part of a city, the harbor area in Hamburg, Germany. We assumed that happiness is a crucial motivating factor for the movement of citizens. A survey of 130 citizens provided the weights that govern the simulated environment and the happiness score assignation of places. As an AI method, we then used Reinforcement Learning as a general model and Q-learning as an algorithm to generate a baseline. Through randomly traversing the model environment a baseline was created. We are in the process of enhancing Reinforcement Learning with a Deep Q-Network to make the actors learn. Early experiments show a significant improvement over a tabular Q-learning approach. This paper contributes to the literature of urban planning, and data-driven architectural design. The main contribution is replacing the inefficient search for a global maximum of the happiness function, with an efficient local solution global maximum. This has implications for further research in the generation of synthetic data through simulations.

关键词： agent-based modeling artificial intelligence city simulations smart cities synthetic data urban design

Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

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Deep Learning for Channel Prediction in Non-Stationary Wirel...

Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems.

关键词： Deep learning Fading channels Adaptation models Attention mechanisms Wireless networks Transfer learning Predictive models data models Kalman filters Long short term memory

SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint

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arXiv 2025年

作者： Yuan, Zhenlong Yang, Zhidong Cai, Yujun Wu, Kuangxin Liu, Mufan Zhang, Dapeng Jiang, Hao Li, Zhaoxin Wang, Zhaoqi The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Australia The Information Technology Department Hunan Police Academy Changsha410100 China Cooperative MediaNet Innovation Center Shanghai Jiao Tong University Shanghai200240 China DSLAB School of Information Science & Engineering Lanzhou University 730000 China Agricultural Information Institute Chinese Academy of Agricultural Sciences Key Laboratory of Agricultural Big Data Ministry of Agriculture and Rural Affairs 100081 China

Recently, patch-deformation methods have exhibited significant effectiveness in multi-view stereo owing to the deformable and expandable patches in reconstructing textureless areas. However, such methods primarily emphasize broadening the receptive field in textureless areas, while neglecting deformation instability caused by easily overlooked edge-skipping, potentially leading to matching distortions. To address this, we propose SED-MVS, which adopts panoptic segmentation and multi-trajectory diffusion strategy for segmentation-driven and edge-aligned patch deformation. Specifically, to prevent unanticipated edge-skipping, we first employ SAM2 for panoptic segmentation as depth-edge guidance to guide patch deformation, followed by multi-trajectory diffusion strategy to ensure patches are comprehensively aligned with depth edges. Moreover, to avoid potential inaccuracy of random initialization, we combine both sparse points from LoFTR and monocular depth map from DepthAnything V2 to restore reliable and realistic depth map for initialization and supervised guidance. Finally, we integrate segmentation image with monocular depth map to exploit inter-instance occlusion relationship, then further regard them as occlusion map to implement two distinct edge constraint, thereby facilitating occlusion-aware patch deformation. Extensive results on ETH3D, Tanks & Temples, BlendedMVS and Strecha datasets validate the state-of-the-art performance and robust generalization capability of our proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Restoration

Optimization of Image Transmission in a Cooperative Semantic Communication Networks

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arXiv 2023年

作者： Zhang, Wenjing Wang, Yining Chen, Mingzhe Luo, Tao Niyato, Dusit The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States NTU Singapore

In this paper, a semantic communication framework for image data transmission is developed. In the investigated framework, a set of servers cooperatively transmit image data to a set of users utilizing semantic communication techniques, which enable servers to transmit only the semantic information that accurately captures the meaning of images. To evaluate the performance of studied semantic communication system, a multimodal metric called image-to-graph semantic similarity (ISS) is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. Due to the co-channel interference among users associated with different servers, each server must cooperate with other servers to find a globally optimal semantic oriented RB allocation. We formulate this problem as an optimization problem whose goal is to minimize the sum of the average transmission latency of each server while reaching the ISS requirement. To solve this problem, we propose a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) algorithm. The proposed algorithm enables each server to coordinate with other servers in training stage and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL algorithms, the proposed RL framework improves the exploration of valuable action of servers and the probability of finding a globally optimal RB allocation policy based on local observation of wireless and semantic communication environments. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% and improve the convergence speed by up to 100% compared to the traditional multi-agent RL algorithms. Copyright © 2

关键词： Cooperative communication

A General Optimization Framework for Tackling Distance Constraints in Movable Antenna-Aided Systems

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arXiv 2025年

作者： Jin, Yichen Lin, Qingfeng Li, Yang Zhu, Hancheng Cheng, Bingyang Wu, Yik-Chung Zhang, Rui Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen518172 China School of Computing and Information Technology Great Bay University Dongguan523000 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

The recently emerged movable antenna (MA) shows great promise in leveraging spatial degrees of freedom to enhance the performance of wireless systems. However, resource allocation in MA-aided systems faces challenges due to the non-convex and coupled constraints on antenna positions. This paper systematically reveals the challenges posed by the minimum antenna separation distance constraints. Furthermore, we propose a penalty optimization framework for resource allocation under such new constraints for MA-aided systems. Specifically, the proposed framework separates the non-convex and coupled antenna distance constraints from the movable region constraints by introducing auxiliary variables. Subsequently, the resulting problem is efficiently solved by alternating optimization, where the optimization of the original variables resembles that in conventional resource allocation problem while the optimization with respect to the auxiliary variables is achieved in closed-form solutions. To illustrate the effectiveness of the proposed framework, we present three case studies: capacity maximization, latency minimization, and regularized zero-forcing precoding. Simulation results demonstrate that the proposed optimization framework consistently outperforms state-of-the-art schemes. © 2025, CC BY-NC-ND.

关键词： Resource allocation