Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-4-29 10:20'''
|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 = This paper presents EMU, a framework that enables the emulation, snipping, and multiplexing of LoRa chirps on commercial IoT devices equipped with low-power sub-GHz transceivers, including those supporting LoRa itself. Chirp snipping consists in artificially removing a sequence of chips and in putting the radio in low-power mode, which allows to reduce energy consumption while still communicating reliably. Chirp multiplexing exploits the gaps introduced by chirp snipping to transmit portions of another chirp on a separate channel, which allows to concurrently transmit two LoRa packets and to increase the throughput. We build EMU as a modular framework and implement support for off-the-shelf LoRa and non-LoRa transceivers. We then evaluate its performance by comparing the reliability, efficiency, and receiver sensitivity achieved by EMU with that of traditional LoRa for different physical layer settings. We finally showcase EMU’s ability to send packets over two channels simultaneously, thereby improving the uplink throughput of LoRaWAN, and demonstrate that even non-LoRa transceivers employing EMU can communicate to a LoRaWAN gateway, enabling new use cases and expanding the applicability of LoRa technology.
|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= IPSN 2022
|confname =SenSys'25
|link=http://www.carloalbertoboano.com/documents/yang22emu.pdf
|link = https://dl.acm.org/doi/10.1145/3715014.3722075
|title= EMU: Increasing the Performance and Applicability of LoRa through Chirp Emulation, Snipping, and Multiplexing
|title= MoLoRa: Intelligent Mobile Antenna System for Enhanced LoRa Reception in Urban Environments
|speaker=Wenliang
|speaker=Kai Chen
|date=2026-1-30
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract = Containers, originally designed for cloud environments, are increasingly popular for provisioning workers outside the cloud, for example in mobile and edge computing. These settings, however, bring new challenges: high latency links, limited bandwidth, and resource-constrained workers. The result is longer provisioning times when deploying new workers or updating existing ones, much of it due to network traffic. Our analysis shows that current piecemeal approaches to reducing provisioning time are not always sufficient, and can even make things worse as round-trip times grow. Rather, we find that the very same layer-based structure that makes containers easy to develop and use also makes it more difficult to optimize deployment. Addressing this issue thus requires rethinking the container deployment pipeline as a whole. Based on our findings, we present Starlight: an accelerator for container provisioning. Starlight decouples provisioning from development by redesigning the container deployment protocol, filesystem, and image storage format. Our evaluation using 21 popular containers shows that, on average, Starlight deploys and starts containers 3.0x faster than the current state-of-the-art implementation while incurring no runtime overhead and little (5%) storage overhead. Finally, it is backwards compatible with existing workers and uses standard container registries.
|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= NSDI 2022
|confname =WWW'25
|link=https://www.usenix.org/system/files/nsdi22-paper-chen_jun_lin.pdf
|link = https://dl.acm.org/doi/abs/10.1145/3696410.3714571
|title=Starlight: Fast Container Provisioning on the Edge and over the WAN
|title= Bridging the Gap: Aligning Language Model Generation with Structured Information Extraction via Controllable State Transition
|speaker=Jiangshu
|speaker=Daobin
|date=2026-1-30
}}
}}
=== 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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