Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2026-01-30 10:30'''
|time='''2026-03-20 10:30'''
|addr=4th Research Building A518
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
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{{Latest_seminar
{{Latest_seminar
|abstract = LoRa technology promises to enable Internet of Things applications over large geographical areas. However, its performance is often hampered by poor channel quality in urban environments, where blockage and multipath effects are prevalent. Our study uncovers that a slight shift in the position or attitude of the receiving antenna can substantially improve the received signal quality. This phenomenon can be attributed to the rich multipath characteristics of wireless signal propagation in urban environments, wherein even small antenna movement can alter the dominant signal path or reduce the polarization angular difference between transceivers. Leveraging these key observations, we propose and implement MoLoRa, an intelligent mobile antenna system designed to enhance LoRa packet reception. At its core, MoLoRa represents the position and attitude of an antenna as a state and employs a statistical optimization method to search for states that offer optimal signal quality efficiently. Through extensive evaluation, we demonstrate that MoLoRa achieves a maximum Signal-to-Noise Ratio (SNR) gain of 13 dB in a few attempts, enabling formerly problematic blind spots to reconnect and strengthening links for other nodes.
|abstract = Satellite-based quantum networks are emerging as a promising solution for the development of a global quantum Internet in the near future. The ability to leverage the advantageous lower attenuation of optical signals from satellites to ground presents an exciting opportunity to establish a robust and secure quantum communication infrastructure on a global scale. By utilizing a constellation of satellites, it becomes feasible to continuously distribute high-fidelity quantum entanglements among ground stations over long distances, overcoming the limitations of traditional terrestrial-based quantum communication systems. In this article, we first provide a brief survey of existing solutions for satellite-based entanglement distribution, highlighting the various approaches and technologies that have been employed in this rapidly evolving field. We then delve into a formulated optimal entanglement distribution problem, aiming to optimize the distribution of quantum entanglement resources across the satellite network to maximize efficiency and reliability. This problem is addressed through a detailed exploration of several different methodologies and algorithms, each tailored to specific operational settings and constraints. Our experimental results confirm the efficiency of these approaches and provide valuable insights into their practical implementation and performance. Finally, we identify several key directions for further study and development in the realm of satellite-based quantum networks.
|confname =SenSys'25
|confname =IEEE Network'25
|link = https://dl.acm.org/doi/10.1145/3715014.3722075
|link = https://ieeexplore.ieee.org/document/10526298
|title= MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments
|title= Optimal Entanglement Distribution Problem in Satellite-Based Quantum Networks
|speaker=Kai Chen
|speaker=Yaliang
|date=2026-1-30
|date=2026-3-20
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract =Large language models (LLMs) achieve superior performance in generative tasks. However, due to the natural gap between language model generation and structured information extraction in three dimensions: task type, output format, and modeling granularity, they often fall short in structured information extraction, a crucial capability for effective data utilization on the web. In this paper, we define the generation process of the language model as the controllable state transition, aligning the generation and extraction processes to ensure the integrity of the output structure and adapt to the goals of the information extraction task. Furthermore, we propose the Structure2Text decider to help the language model understand the fine-grained extraction information, which converts the structured output into natural language and makes state decisions, thereby focusing on the task-specific information kernels, and alleviating language model hallucinations and incorrect content generation. We conduct extensive experiments and detailed analyses on myriad information extraction tasks, including named entity recognition, relation extraction, and event argument extraction. Our method not only achieves significant performance improvements but also considerably enhances the model's capability to generate precise and relevant content, making the extracted content easy to parse.
|abstract =Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g., remote object detection) typically require multi-satellite cooperative observations of a large region of interest (RoI) area, as well as the observation image routing and computation processing, enabling accurate and real-time responsiveness. However, optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties. To this end, we propose SECO, a SEC-enabled framework that jointly optimizes multi-satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in the observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game-based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed systemwide greedy-based strategy to reduce the total time cost (i.e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real-world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines.
|confname =WWW'25
|confname =INFOCOM'24
|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
|link = https://ieeexplore.ieee.org/document/10621270
|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
|title= SECO: Multi-Satellite Edge Computing Enabled Wide-Area and Real-Time Earth Observation Missions
|speaker=Daobin
|speaker=LinQi
|date=2026-1-30
|date=2026-3-20
}}
}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 01:27, 20 March 2026

Time: 2026-03-20 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [IEEE Network'25] Optimal Entanglement Distribution Problem in Satellite-Based Quantum Networks, Yaliang
    Abstract: Satellite-based quantum networks are emerging as a promising solution for the development of a global quantum Internet in the near future. The ability to leverage the advantageous lower attenuation of optical signals from satellites to ground presents an exciting opportunity to establish a robust and secure quantum communication infrastructure on a global scale. By utilizing a constellation of satellites, it becomes feasible to continuously distribute high-fidelity quantum entanglements among ground stations over long distances, overcoming the limitations of traditional terrestrial-based quantum communication systems. In this article, we first provide a brief survey of existing solutions for satellite-based entanglement distribution, highlighting the various approaches and technologies that have been employed in this rapidly evolving field. We then delve into a formulated optimal entanglement distribution problem, aiming to optimize the distribution of quantum entanglement resources across the satellite network to maximize efficiency and reliability. This problem is addressed through a detailed exploration of several different methodologies and algorithms, each tailored to specific operational settings and constraints. Our experimental results confirm the efficiency of these approaches and provide valuable insights into their practical implementation and performance. Finally, we identify several key directions for further study and development in the realm of satellite-based quantum networks.
  2. [INFOCOM'24] SECO: Multi-Satellite Edge Computing Enabled Wide-Area and Real-Time Earth Observation Missions, LinQi
    Abstract: Rapid advances in low Earth orbit (LEO) satellite technology and satellite edge computing (SEC) have facilitated a key role for LEO satellites in enhanced Earth observation missions (EOM). These missions (e.g., remote object detection) typically require multi-satellite cooperative observations of a large region of interest (RoI) area, as well as the observation image routing and computation processing, enabling accurate and real-time responsiveness. However, optimizing the resources of LEO satellite networks is nontrivial in the presence of its dynamic and heterogeneous properties. To this end, we propose SECO, a SEC-enabled framework that jointly optimizes multi-satellite observation scheduling, routing and computation node selection for enhanced EOM. Specifically, in the observation phase, we leverage the orbital motion and the rotatable onboard cameras of satellites, and propose a distributed game-based scheduling strategy to minimize the overall size of captured images while ensuring full (observation) coverage. In the sequent routing and computation phase, we first adopt image splitting technology to achieve parallel transmission and computation. Then, we propose an efficient iterative algorithm to jointly optimize image splitting, routing and computation node selection for each captured image. On this basis, we propose a theoretically guaranteed systemwide greedy-based strategy to reduce the total time cost (i.e., transmission, computation and queuing delay) over simultaneous processing for multiple images. Extensive experiments based on real-world datasets demonstrate that SECO can achieve up to a 60.7% reduction in overall time cost compared to baselines.

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

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