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Beyond Radio: Het-Medium Networks and Its Cross-Medium Communication Paradigms for 6G

IEEE Network 2025年

作者： Wu, Mingming Han, Yufeng Xiao, Yue Gao, Yulan Li, Shiying Niyato, Dusit Karagiannidis, George K. University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China QianYuan National Laboratory Hangzhou310024 China KTH Royal Institute of Technology Division of Information Science and Engineering Stockholm100 44 Sweden Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Group Electrical and Computer Engineering Dept Thessaloniki54 124 Greece

In the evolving landscape of communication technologies, the integration of Heterogeneous Medium Networks (HetMNets) has emerged as a pivotal progression, signaling a departure from traditional, radio-centric paradigms. As we edge closer to the realization of 6G networks, understanding the intricate dynamics of cross-medium communication (CMC) becomes imperative. This article indicates the multifaceted nature of HetMNets and explores the transformative CMC paradigms shaping the roadmap toward 6G. First, we outline the key performance indicators (KPIs), potential scenarios, and the emerging technologies and applications of 6G wireless systems, which serve as the driving forces behind the development of HetMNets. Subsequently, various communication media are introduced, contrasting their propagation characteristics to underscore the indispensability of HetMNets for the 6G landscape. We then enumerate the general issues involving challenges/problems and potential technical solutions in HetMNets. Moreover, we present an air-sea coordinated CMC system model, serving as an example of HetMNets built upon CMC paradigms. Specifically, we tackle a power allocation challenge, optimizing the transmit power of both underwater acoustic and radio frequency (RF) communication media at the cross-medium bidirectional relay to curtail the outage probability. The corresponding solution highlights the efficacy of the proposed CMC paradigm in supporting the reliability and resource efficiency of HetMNets. © 1986-2012 IEEE.

关键词： Radio communication

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Training Physics- Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction

Training Physics- Informed Neural Networks via Multi-Task Op...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Bo Wang A. K. Qin Sajjad Shafiei Hussein Dia Adriana-Simona Mihaita Hanna Grzybowska Dept. of Computing Technologies Swinburne University of Technology Melbourne Australia Dept. of Civil and Construction Engineering Swinburne University of Technology Melbourne Australia Data Science Institute University of Technology Sydney Sydney Australia Simulation Group Data 61|CSIRO Sydney Australia

Physics-informed neural networks (PINN s) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e.g., those described by partial differential equations (PDEs), into the training of the neural network (NN) based on such a data set. In PINN s, the NN acts as the solution approximator for the PDE while the PDE acts as the prior knowledge to guide the NN training, leading to the desired generalization performance of the NN when facing the limited availability of training data. However, training PINNs is a non-trivial task largely due to the complexity of the loss composed of both NN and physical law parts. In this work, we propose a new PINN training framework based on the multi-task optimization (MTO) paradigm. Under this framework, multiple auxiliary tasks are created and solved together with the given (main) task, where the useful knowledge from solving one task is transferred in an adaptive mode to assist in solving some other tasks, aiming to uplift the performance of solving the main task. We implement the proposed framework and apply it to train the PINN for addressing the traffic density prediction problem. Experimental results demonstrate that our proposed training framework leads to significant performance improvement in comparison to the traditional way of training the PINN.

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Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction

arXiv

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arXiv 2023年

作者： Wang, Bo Qin, A.K. Shafiei, Sajjad Dia, Hussein Mihaita, Adriana-Simona Grzybowska, Hanna Dept. of Computing Technologies Swinburne University of Technology Melbourne Australia Dept. of Civil and Construction Engineering Swinburne University of Technology Melbourne Australia Data Science Institute University of Technology Sydney Sydney Australia Simulation Group Data 61 CSIRO Sydney Australia

Physics-informed neural networks (PINNs) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e.g., those described by partial differential equations (PDEs), into the training of the neural network (NN) based on such a data set. In PINNs, the NN acts as the solution approximator for the PDE while the PDE acts as the prior knowledge to guide the NN training, leading to the desired generalization performance of the NN when facing the limited availability of training data. However, training PINNs is a non-trivial task largely due to the complexity of the loss composed of both NN and physical law parts. In this work, we propose a new PINN training framework based on the multi-task optimization (MTO) paradigm. Under this framework, multiple auxiliary tasks are created and solved together with the given (main) task, where the useful knowledge from solving one task is transferred in an adaptive mode to assist in solving some other tasks, aiming to uplift the performance of solving the main task. We implement the proposed framework and apply it to train the PINN for addressing the traffic density prediction problem. Experimental results demonstrate that our proposed training framework leads to significant performance improvement in comparison to the traditional way of training the PINN. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model

arXiv

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arXiv 2023年

作者： Shidqi, Naufal Jeong, Chaeyoon Park, Sungwon Zeller, Elke Nellikkattil, Arjun Babu Singh, Karandeep School of Computing KAIST Daejeon Korea Republic of Data Science Group IBS Daejeon Korea Republic of Department of Climate System PNU Busan Korea Republic of Center for Climate Physics IBS Busan Korea Republic of

Climate downscaling is a crucial technique within climate research, serving to project low-resolution (LR) climate data to higher resolutions (HR). Previous research has demonstrated the effectiveness of deep learning for downscaling tasks. However, most deep learning models for climate downscaling may not perform optimally for high scaling factors (i.e., 4x, 8x) due to their limited ability to capture the intricate details required for generating HR climate data. Furthermore, climate data behaves differently from image data, necessitating a nuanced approach when employing deep generative models. In response to these challenges, this paper presents a deep generative model for downscaling climate data, specifically precipitation on a regional scale. We employ a denoising diffusion probabilistic model (DDPM) conditioned on multiple LR climate variables. The proposed model is evaluated using precipitation data from the Community Earth System Model (CESM) v1.2.2 simulation. Our results demonstrate significant improvements over existing baselines, underscoring the effectiveness of the conditional diffusion model in downscaling climate data. © 2023, CC BY-NC-SA.

关键词： Earth system models

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SPA: a graph spectral alignment perspective for domain adaptation 23

SPA: a graph spectral alignment perspective for domain adapt...

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

作者： Zhiqing Xiao Haobo Wang Ying Jin Lei Feng Gang Chen Fei Huang Junbo Zhao College of Computer Science and Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University CUHK-SenseTime Joint Lab The Chinese University of Hong Kong School of Computer Science and Engineering Nanyang Technological University Alibaba Group

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a finegrained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA.

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Neural Classification of Terrestrial Biomes

Neural Classification of Terrestrial Biomes

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International Conference on Big data and Smart computing (BIGCOMP)

作者： Vyacheslav Shen Dong-Kyum Kim Elke Zeller Meeyoung Cha School of Computing KAIST Daejeon South Korea Data Science Group IBS Daejeon South Korea Center for Climate Physics IBS Busan South Korea Department of Climate System PNU Busan South Korea

Predicting vegetation changes under climate change is crucial because it will alter the distribution of different plants and have repercussions for ecosystems. To detect changes in vegetation, we employ biome classification that assigns vegetation distributions to specific biomes. Conventional methods have used empirical formulas or simple vegetation models. Based on previous research that showed the use of convolutional neural networks (CNN), this work employs multiple deep models to classify biomes with the goal of predicting future changes. Experiments over multiple datasets demonstrate that Transformer models can be a suitable alternative to the CNN model. In addition, we observe that the use of additional climate variables helps improve the prediction accuracy without overfitting the data, which previous studies have not considered. We discuss the future directions of machine learning for biome classification as a complement to traditional biome classification methods.

关键词： Climate change Classification Vegetation mapping Predictive models Convolutional neural networks Meteorology Machine learning Ecosystems Biomes

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Self-supervised learning for climate downscaling

Self-supervised learning for climate downscaling

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International Conference on Big data and Smart computing (BIGCOMP)

作者： Karandeep Singh Chaeyoon Jeong Sungwon Park Arjun N Babur Elke Zeller Meeyoung Cha Data Science Group IBS Daejeon South Korea School of Computing KAIST Daejeon South Korea Center for Climate Physics IBS Busan South Korea Department of Climate System PNU Busan South Korea

Earth system models (ESM) are computer models that quantitatively simulate the Earth’s climate system. These models are the basis of modern research on climate change and its effects on our planet. Advances in computational technologies and simulation methodologies have enabled ESM to produce simulation outputs at a finer level of detail, which is important for policy planning and research at the regional level. As ESM is a complex incorporation of different physical domains and environmental variables, computational costs for conducting simulations at a finer resolution are prohibitively expensive. In practice, the simulation at the coarser level is mapped onto the regional level by the process of ‘‘downscaling’’. In this presents a self-supervised deep-learning solution for climate downscaling that does not require high-resolution ground truth data during the model training process. We introduce a self-supervised convolutional neural network (CNN) super-resolution model that trains on a single data instance at a time and can adapt to its underlying data patterns at runtime. Experimental results demonstrate that the proposed model consistently improves the climate downscaling performance over the widely used baselines by a large margin.

关键词： Self-supervised learning Climate change Modeling Earth Computer simulation

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Classification of Cybercrime Indicators in Open Social data 7th

Classification of Cybercrime Indicators in Open Social Data

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7th Annual International Conference on Information Management and Big data, SIMBig 2020

作者： Ullah, Ihsan Lane, Caoilfhionn Buda, Teodora Sandra Drury, Brett Mellotte, Marc Assem, Haytham Madden, Michael G. Computer Science National University of Ireland Galway Galway Ireland Insight Center for Data Analytics National University of Ireland Galway Galway Ireland Cognitive Computing Group Innovation Exchange IBM Dublin Ireland

ISBN: (纸本)9783030762278

Posting information on social media platforms is a popular activity through which personal and confidential information can leak into the public domain. Consequently, social media can contain information that provides an indication that an organization has been compromised or suffered a data breach. This paper describes a technique for inferring if an organization has been compromised from information posted on social media. The proposed strategy forms the basis of an alarm system which generates an alert for possible unreported cybercrime incidents. The proposed strategy used two social media cybercrime related datasets that were collected from the Irish and New York regions from financial organizations’ Twitter accounts. The Tweets are labelled as either containing cybercrime indicators or not, and then the cybercrime Tweets were labelled further into crime categories. A deep dense pyramidal Neural Network model is used to classify the Tweets. This approach achieves an AUC of 0.85±0.03 which outperforms the baseline of deep convolutional neural networks. © 2021, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Elsa: Energy-based learning for semi-supervised anomaly detection

arXiv

引用

arXiv 2021年

作者： Han, Sungwon Song, Hyeonho Lee, Seungeon Park, Sungwon Cha, Meeyoung School of Computing KAIST Korea Republic of Data Science Group IBS Korea Republic of

Anomaly detection aims at identifying deviant instances from the normal data distribution. Many advances have been made in the field, including the innovative use of unsupervised contrastive learning. However, existing methods generally assume clean training data and are limited when the data contain unknown anomalies. This paper presents Elsa, a novel semi-supervised anomaly detection approach that unifies the concept of energy-based models with unsupervised contrastive learning. Elsa instills robustness against any data contamination by a carefully designed fine-tuning step based on the new energy function that forces the normal data to be divided into classes of prototypes. Experiments on multiple contamination scenarios show the proposed model achieves SOTA performance. Extensive analyses also verify the contribution of each component in the proposed model. Beyond the experiments, we also offer a theoretical interpretation of why contrastive learning alone cannot detect anomalies under data contamination. Copyright © 2021, The Authors. All rights reserved.

关键词： Anomaly detection

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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

arXiv

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arXiv 2023年

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra Department of Data Science New Jersey Institute of Technology United States Optimization and Control Group Pacific Northwest National Laboratory United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models. © 2023, CC BY-NC-SA.

关键词： Decision making

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