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UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning

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arXiv 2024年

作者： Liu, Saichao Sun, Geng Li, Jiahui Liang, Shuang Wu, Qingqing Wang, Pengfei Niyato, Dusit The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Information Science and Technology Northeast Normal University Changchun130117 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Dalian University of Technology Dalian116024 China

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

CoRAL: a Context-aware Croatian Abusive Language dataset

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arXiv 2022年

作者： Shekhar, Ravi Karan, Mladen Purver, Matthew Cognitive Science Research Group School of Electronic Engineering & Computer Science Queen Mary University of London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying the examples or allowing the moderators to prioritize which comments to consider first. However, the concept of inappropriate content is often subjective, and such content can be conveyed in many subtle and indirect ways. In this work, we propose CoRAL - a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context. We show experimentally that current models degrade when comments are not explicit and further degrade when language skill and context knowledge are required to interpret the comment. © 2022, CC BY-NC-ND.

关键词： Moderators

Split Moves for Monte-Carlo Tree Search

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arXiv 2021年

作者： Kowalski, Jakub Mika, Maksymilian Pawlik, Wojciech Sutowicz, Jakub Szykula, Marek Winands, Mark H.M. University of Wroclaw Faculty of Mathematics and Computer Science Poland Maastricht University Department of Data Science and Knowledge Engineering Netherlands

In many games, moves consist of several decisions made by the player. These decisions can be viewed as separate moves, which is already a common practice in multi-action games for efficiency reasons. Such division of a player move into a sequence of simpler / lower level moves is called splitting. So far, split moves have been applied only in forementioned straightforward cases, and furthermore, there was almost no study revealing its impact on agents’ playing strength. Taking the knowledge-free perspective, we aim to answer how to effectively use split moves within Monte-Carlo Tree Search (MCTS) and what is the practical impact of split design on agents’ strength. This paper proposes a generalization of MCTS that works with arbitrarily split moves. We design several variations of the algorithm and try to measure the impact of split moves separately on efficiency, quality of MCTS, simulations, and action-based heuristics. The tests are carried out on a set of board games and performed using the Regular Boardgames General Game Playing formalism, where split strategies of different granularity can be automatically derived based on an abstract description of the game. The results give an overview of the behavior of agents using split design in different ways. We conclude that split design can be greatly beneficial for single- as well as multi-action games. © 2021, CC BY.

关键词： Efficiency

UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

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arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

Exploring Pre-Trained Neural Audio Representations for Audio Topic Segmentation

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Exploring Pre-Trained Neural Audio Representations for Audio...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Iacopo Ghinassi Matthew Purver Huy Phan Chris Newell Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London UK Centre for Digital Music School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Amazon Alexa Cambridge MA USA BBC R&D UK

Recent works have shown that audio embeddings can improve automatic topic segmentation of formats such as radio shows. In this work we expand the work in that direction by showing how and which publicly available, pre-trained neural audio embeddings can perform the task, without the need of any further fine-tuning of the audio encoders. The ranking of the encoders suggest that neural encoders pre-trained for speaker diarization and general purpose audio classification are the best suited to be used as features, beating non-neural baselines. We show that we can obtain perfect results on a newly created random dataset similar to the one used in previous work. We also show for the first time results on real-world data, proving that our method can be applied to actual radio shows with good results, but the choice of audio encoders is extremely important in order to achieve those. Finally, by releasing the datasets we used we make the contribution of providing the first (to our knowledge) publicly available, free of charge datasets for audio topic segmentation of media products.

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Winning the carbon race: Game-based strategies for emission reduction in maritime industry

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Research in Transportation Business and Management 2025年 61卷

作者： Ruslan, Siti Marsila Mhd Osman, Mohd Tarmizi Mansor, Wan Nurdiyana Wan Mokhtar, Kasypi Saidi, Norjipin Roslan, Ummu Atiqah Mohd Hassim, Muhamad Fairus Noor Mclellan, Benjamin Craig Faculty of Maritime Studies Universiti Malaysia Terengganu Malaysia Security Safety & Environmental Division Marine Department Malaysia Faculty of Ocean Engineering Technology Universiti Malaysia Terengganu Malaysia Special Interest Group on Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Malaysia Special Interest Group on Modelling and Data Analytics Faculty Science and Marine Environment Universiti Malaysia Terengganu Malaysia Energy Economics Research Group Graduate School of Energy Science Department of Socio-Environmental Energy Science Kyoto University Japan

The maritime industry contributes to global greenhouse gas (GHG) emissions, accounting for approximately 2.89 % of global emissions. With the International Maritime Organization (IMO) setting ambitious goals to reduce carbon emissions by 50 % by 2050, there is a pressing need for effective strategies in the maritime industry. This study examines the strategic interactions between governments, port authorities, and shipping companies by utilizing game theory to explore dynamic and evolving behaviors in carbon emission reduction efforts. By establishing a trilateral game model, the research provides insights into the complex interplay of incentives and deterrents that influence stakeholder actions. Key scenarios analyzed include the net incremental benefits of adopting specific strategies by port authorities and the additional costs incurred by shipping companies when implementing green practices. The findings indicate that port authorities can achieve significant benefits by adopting collaborative strategies, supported by government incentives to offset initial high costs, while for shipping companies, the analysis underscores the importance of weighing additional costs against potential operational efficiencies and market advantages. The study offers targeted policy recommendations to promote the development of a low-carbon maritime industry, highlighting the potential of green fuels and advanced technologies in achieving sustainable maritime operations. This research fills a critical gap in understanding the strategic behaviors driving emission reductions in maritime industry, contributing to broader efforts in combating global climate change. © 2025 Elsevier Ltd

关键词： Game theory GHG emission Government IMO Port Shipping

Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization

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arXiv 2025年

作者： Wu, Weiqi Huang, Shen Jiang, Yong Xie, Pengjun Huang, Fei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongyi Lab Alibaba Group China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS). By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round. Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web. Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

PROV-IO+: A Cross-Platform Provenance Framework for Scientific data on HPC Systems

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arXiv 2023年

作者： Han, Runzhou Zheng, Mai Byna, Suren Tang, Houjun Dong, Bin Dai, Dong Chen, Yong Kim, Dongkyun Hassoun, Joseph Thorsley, David Wolf, Matthew Department of Electrical and Computer Engineering Iowa State University United States Department of Computer Science and Engineering The Ohio State University United States Scientific Data Management Group Lawrence Berkeley National Laboratory United States Computer Science Department University of North Carolina Charlotte United States Systems Architecture Lab Samsung United States Samsung Advanced Institute of Technology Samsung Korea Republic of

data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments. © 2023, CC BY-NC-SA.

关键词： Life cycle

Effects of Optimized Operating Parameters on Combustion Characteristics and NO_(x)Emissions of a Burner based on Orthogonal Analysis

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Journal of Thermal science 2021年第4期30卷 1212-1223页

作者： XU Qian AKKURT Nevzat YANG Gang ZHU Lidong SHI Kejian WANG Kang ZOU Zhenwei LIU Zhihui WANG Jiulong DU Zhiwei School of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory for Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China AVIC Xinxiang Aviation Industry(group)CO. LTDHenan 453002China

To optimize the structure of the burner,improve the combustion performance,and reduce the emission of NO_(x),a self-circulating low NO_(x)combustion technology was used to design a new type of flue gas self-circulating low NO_(x)*** on previous research on the numerical model of combustion and the composition of mixed gas on combustion and NO_(x)emissions,the effect of various factors on the ejection coefficient of the flue gas self-circulating structure was analyzed using the orthogonal test method,and the burner operating parameters,such as preheating temperature and excess air coefficient,were deeply studied through the three-dimensional finite element numerical model in this *** results show that the diameter ratio of the nozzle and the length of the cylindrical section of the flue gas self-circulating structure have great influence on its ejection and mixing *** optimal ejection coefficient was ***,the amount of NO_(x)emissions greatly increased from 6.23×10^(-6)(volume fraction)at the preheating temperature 973 K to 3.5×10^(-3)at preheating temperature 1573 *** the excess air coefficient decreased from 1.2 to 1,the maximum combustion temperature decreased from 2036.3 K to 1954.22 K,and the NO_(x)emissions decreased from 352.29×10^(-6)to 159.73×10^(-6).

关键词： preheating temperature excess air coefficient self-circulating flue gas low oxygen combustion low-NO_(x)emissions