Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-05-04 9:30'''
|time='''2026-01-30 10:30'''
|addr=4th Research Building A527-B
|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]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=In vehicular ad hoc networks (VANETs), quick and reliable multi-hop broadcasting is important for the dissemination of emergency warning messages. By scheduling multiple nodes to transmit messages concurrently and cooperatively, cooperative transmission based broadcast schemes may yield much better broadcast performance than conventional broadcast schemes. However, a cooperative transmission requires multiple relays to achieve strict synchronization on both time and frequency, which may induce high cost for a cooperative transmission process. In this paper, we analyze the cost and benefit of a cooperative transmission for data broadcasting in vehicular networks, and introduce a new metric called the single-hop broadcast efficiency (SBE) to evaluate the overall broadcast performance. We propose an efficient, non-deterministic cooperation mechanism to reduce the cooperation cost. The mechanism maximizes the expected broadcast performance by selecting cooperators with the largest expected SBE value for a lead relay, and initiates cooperative broadcasting process when the expected SBE value is larger than that of a single-relay based broadcasting. Based on the non-deterministic mechanism, we propose an efficient, cooperative transmission based opportunistic broadcast (ECTOB) scheme which further utilizes rebroadcast to improve the reliability of the broadcast scheme. Simulation results show that the proposed scheme outperforms the conventional ones.
|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.
|confname=TMC 2023
|confname =SenSys'25
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9519523
|link = https://dl.acm.org/doi/10.1145/3715014.3722075
|title=An Efficient Cooperative Transmission Based Opportunistic Broadcast Scheme in VANETs
|title= MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments
|speaker=Luwei}}
|speaker=Kai Chen
|date=2026-1-30
}}
{{Latest_seminar
{{Latest_seminar
|abstract = Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local model update in client, ignoring the performance drop caused by direct global model aggregation. Instead, we propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG), which relieves the issue of direct model aggregation. Concretely, FedFTG explores the input space of local models through a generator, and uses it to transfer the knowledge from local models to the global model. Besides, we propose a hard sample mining scheme to achieve effective knowledge distillation throughout the training. In addition, we develop customized label sampling and class-level ensemble to derive maximum utilization of knowledge, which implicitly mitigates the distribution discrepancy across clients. Extensive experiments show that our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
|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.
|confname=CVPR 2022
|confname =WWW'25
|link=https://arxiv.org/pdf/2203.09249.pdf
|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
|title=Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning
|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
|speaker=Jiaqi}}
|speaker=Daobin
{{Latest_seminar
|date=2026-1-30
|abstract = Visible light communication (VLC) systems relying on commercial-off-the-shelf (COTS) devices have gathered momentum recently, due to the pervasive adoption of LED lighting and mobile devices. However, the achievable throughput by such practical systems is still several orders below those claimed by controlled experiments with specialized devices. In this paper, we engineer CoLight aiming to boost the data rate of the VLC system purely built upon COTS devices. CoLight adopts COTS LEDs as its transmitter, but it innovates in its simple yet delicate driver circuit wiring an array of LED chips in a combinatorial manner. Consequently, modulated signals can directly drive the on-off procedures of individual chip groups, so that the spatially synthesized light emissions exhibit a varying luminance following exactly the modulation symbols. To obtain a readily usable receiver, CoLight interfaces a COTS PD with a smartphone through the audio jack, and it also has an alternative MCU-driven circuit to emulate a future integration into the phone. The evaluations on CoLight are both promising and informative: they demonstrate a throughput up to 80 kbps at a distance of 2 m, while suggesting various potentials to further enhance the performance.judiciously allocating 15.81 -- 37.67% idle resources on frames that tend to yield greater marginal benefits from enhancement.
}}
|confname=TMC 2021
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8978742
|title=Pushing the Data Rate of Practical VLC via Combinatorial Light Emission
|speaker=Mengyu}}
 
 
 
=== History ===
 
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 10:51, 30 January 2026

Time: 2026-01-30 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [SenSys'25] MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments, Kai Chen
    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.
  2. [WWW'25] Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition, Daobin
    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.

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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