Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-5-9 10:30'''
|time='''2025-12-05 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 = We report XLINK, a multi-path QUIC video transport solution with experiments in Taobao short videos. XLINK is designed to meet two operational challenges at the same time: (1) Optimized user-perceived quality of experience (QoE) in terms of robustness, smoothness, responsiveness, and mobility and (2) Minimized cost overhead for service providers (typically CDNs). The core of XLINK is to take the opportunity of QUIC as a user-space protocol and directly capture user-perceived video QoE intent to control multi-path scheduling and management. We overcome major hurdles such as multi-path head-of-line blocking, network heterogeneity, and rapid link variations and balance cost and performance. To the best of our knowledge, XLINK is the first large-scale experimental study of multi-path QUIC video services in production environments. We present the results of over 3 million e-commerce product short-video plays from consumers who upgraded to Taobao android app with XLINK. Our study shows that compared to single-path QUIC, XLINK achieved 19 to 50% improvement in the 99-th percentile video-chunk request completion time, 32% improvement in the 99-th percentile first-video-frame latency, 23 to 67% improvement in the re-buffering rate at the expense of 2.1% redundant traffic.
|abstract = Intermediate reasoning or acting steps have successfully improved large language models (LLMs) for handling various downstream natural language processing (NLP) tasks. When applying LLMs for code generation, recent works mainly focus on directing the models to articulate intermediate natural-language reasoning steps, as in chain-of-thought (CoT) prompting, and then output code with the natural language or other structured intermediate steps. However, such output is not suitable for code translation or generation tasks since the standard CoT has different logical structures and forms of expression with the code. In this work, we introduce the universal code (UniCode) as the intermediate representation. It is a description of algorithm steps using a mix of conventions of programming languages, such as assignment operator, conditional operator, and loop. Hence, we collect an instruction dataset UniCoder-Instruct to train our model UniCoder on multi-task learning objectives. UniCoder-Instruct comprises natural-language questions, code solutions, and the corresponding universal code. The alignment between the intermediate universal code representation and the final code solution significantly improves the quality of the generated code. The experimental results demonstrate that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin, showcasing the effectiveness of the structural clues in pseudo-code.
|confname= SIGCOMM 2021
|confname =ACL'24
|link=https://dl.acm.org/doi/pdf/10.1145/3452296.3472893
|link = https://arxiv.org/abs/2406.16441
|title= XLINK: QoE-driven multi-path QUIC transport in large-scale video services
|title= UniCoder: Scaling Code Large Language Model via Universal Code
|speaker=Rong
|speaker=Bairong Liu
|date=2025-12-05
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract = In wireless sensor networks (WSNs), provenance is critical for assessing the trustworthiness of the data acquired and forwarded by sensor nodes. Due to the energy and bandwidth limitations of WSNs, it is crucial that data provenance should be as compact as possible. The main drawback of the existing block provenance schemes is that to decode the provenance, all of the provenance blocks must be received by the base station (BS) correctly. To address such an issue, we propose a multi-granularity graphs based stepwise refinement provenance scheme (MSRP), among which we use the mutual information between node pair as the similarity index to classify node IDs and then generate the multi-granularity topology graphs. Furthermore, the dictionary-based provenance scheme (DP) is employed to encode the provenance in a stepwise manner. The BS recovers the provenance in the same stepwise manner and performs the data trustworthiness evaluation during the decoding. We evaluate the performance of the MSRP scheme extensively by both simulations and testbed experiments. In addition to mitigating the main drawback of the existing block provenance schemes, the results show that our scheme not only outperforms the known related schemes with respect to average provenance size and energy consumption, but also drastically improves data trustworthiness assessing efficiency.
|abstract =LoRaWANs are envisioned to connect billions of IoT devices through thousands of physically overlapping yet logically orthogonal channels (termed logical channels). These logical channels hold significant potential for enabling highly concurrent scalable IoT connectivity. Large-scale deployments however face strong interference between logical channels. This practical issue has been largely overlooked by existing works but becomes increasingly prominent as LoRaWAN scales up. To address this issue, we introduce Canas, an innovative gateway design that is poised to orthogonalize the logical channels by eliminating mutual interference. To this end, Canas develops a series of novel solutions to accurately extract the meta-information of individual ultra-weak LoRa signals from the received overlapping channels. The meta-information is then leveraged to accurately reconstruct and subtract the LoRa signals over thousands of logical channels iteratively. Real-world evaluations demonstrate that Canas can enhance concurrent transmissions across overlapping logical channels by 2.3× compared to the best known related works.
|confname= IoTJ 2021
|confname =TMC'25
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9612588
|link = https://ieeexplore.ieee.org/abstract/document/11160677
|title=Stepwise Refinement Provenance Scheme for Wireless Sensor Networks
|title= Resolving Inter-Logical Channel Interference for Large-scale LoRa Deployments
|speaker=Zhuoliu
|speaker=Mengyu
|date=2025-12-05
}}
}}
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 09:25, 5 December 2025

Time: 2025-12-05 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [ACL'24] UniCoder: Scaling Code Large Language Model via Universal Code, Bairong Liu
    Abstract: Intermediate reasoning or acting steps have successfully improved large language models (LLMs) for handling various downstream natural language processing (NLP) tasks. When applying LLMs for code generation, recent works mainly focus on directing the models to articulate intermediate natural-language reasoning steps, as in chain-of-thought (CoT) prompting, and then output code with the natural language or other structured intermediate steps. However, such output is not suitable for code translation or generation tasks since the standard CoT has different logical structures and forms of expression with the code. In this work, we introduce the universal code (UniCode) as the intermediate representation. It is a description of algorithm steps using a mix of conventions of programming languages, such as assignment operator, conditional operator, and loop. Hence, we collect an instruction dataset UniCoder-Instruct to train our model UniCoder on multi-task learning objectives. UniCoder-Instruct comprises natural-language questions, code solutions, and the corresponding universal code. The alignment between the intermediate universal code representation and the final code solution significantly improves the quality of the generated code. The experimental results demonstrate that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin, showcasing the effectiveness of the structural clues in pseudo-code.
  2. [TMC'25] Resolving Inter-Logical Channel Interference for Large-scale LoRa Deployments, Mengyu
    Abstract: LoRaWANs are envisioned to connect billions of IoT devices through thousands of physically overlapping yet logically orthogonal channels (termed logical channels). These logical channels hold significant potential for enabling highly concurrent scalable IoT connectivity. Large-scale deployments however face strong interference between logical channels. This practical issue has been largely overlooked by existing works but becomes increasingly prominent as LoRaWAN scales up. To address this issue, we introduce Canas, an innovative gateway design that is poised to orthogonalize the logical channels by eliminating mutual interference. To this end, Canas develops a series of novel solutions to accurately extract the meta-information of individual ultra-weak LoRa signals from the received overlapping channels. The meta-information is then leveraged to accurately reconstruct and subtract the LoRa signals over thousands of logical channels iteratively. Real-world evaluations demonstrate that Canas can enhance concurrent transmissions across overlapping logical channels by 2.3× compared to the best known related works.

History

|abstract =The rapid expansion of large language models (LLMs) requires the development of extensive GPU clusters, with companies deploying clusters with tens to hundreds of thousands of GPUs. This growth significantly expands the design space for LLM training systems, requiring thorough exploration of different parallelization strategies, communication parameters, congestion control, fabric topology, etc. Current methods require up to 10k simulation experiments to identify optimal configurations, with inadequate exploration leading to significant degradation of training performance. In this paper, we tackle the overlooked problem of efficiently conducting parallel simulation experiments for design space exploration. Our

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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