Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''Friday 10:30-12:00'''
|time='''2025-12-12 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]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=Connected autonomous vehicles have boosted a high demand on communication throughput in order to timely share the information collected by in-car sensors (e.g., LiDAR). While visible light communication (VLC) has shown its capability to offer Gigabit-level throughput for applications with high demand for data rate, most are performed indoors and the throughput of outdoor VLC drops to a few Mbps. To fill this performance gap, this paper presents RayTrack, an interference-free outdoor mobile VLC system. The key idea of RayTrack is to use a small but real-time adjustable FOV according to the transmitter location, which can effectively repel interference from the environment and from other transmitters and boost the system throughput. The idea also realizes virtual point-to-point links, and eliminates the need of link access control. To be able to minimize the transmitter detection time to only 20 ms, RayTrack leverages a high-compression-ratio compressive sensing scheme, incorporating a dual-photodiode architecture, optimized measurement matrix and Gaussian-based basis to increase sparsity. Real-world driving experiments show that RayTrack is able to achieve a data rate of 607.9 kbps with over 90% detection accuracy and lower than 15% bit error rate at 35 m, with 70 - 100 km/hr driving speed. To the best of our knowledge, this is the first working outdoor VLC system which can offer such range, throughput and error performance while accommodating freeway mobility.
|abstract = Code translation is a crucial activity in the software development and maintenance process, and researchers have recently begun to focus on using pre-trained large language models (LLMs) for code translation. However, existing LLMs only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code, which results in unguaranteed code executability and unreliable automated code translation. To address this issue, we propose ExeCoder, an LLM specifically designed for code translation, aimed at utilizing executability representations such as functional semantics, syntax structures, and variable dependencies to enhance the capabilities of LLMs in code translation. To evaluate the effectiveness of ExeCoder, we manually enhanced the widely used benchmark TransCoder-test, resulting in a benchmark called TransCoder-test-X that serves LLMs. Evaluation of TransCoder-test-X indicates that ExeCoder achieves state-of-the-art performance in code translation, surpassing existing open-source code LLMs by over 10.88% to 38.78% and over 27.44% to 42.97% on two metrics, and even outperforms the renowned closed-source LLM GPT-4o.  
|confname=MobiSys'21
|confname =EMNLP'25
|link=https://dl.acm.org/doi/10.1145/3458864.3466867
|link = https://arxiv.org/abs/2501.18460
|title=RayTrack: enabling interference-free outdoor mobile VLC with dynamic field-of-view
|title= ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation
|speaker=Mengyu
|speaker=Youwei Ran
|date=2024-06-07}}
|date=2025-12-12
}}
{{Latest_seminar
{{Latest_seminar
|abstract=Volumetric videos offer viewers more immersive experiences, enabling a variety of applications. However, state-of-the-art streaming systems still need hundreds of Mbps, exceeding the common bandwidth capabilities of mobile devices. We find a research gap in reusing inter-frame redundant information to reduce bandwidth consumption, while the existing inter-frame compression methods rely on the so-called explicit correlation, i.e., the redundancy from the same/adjacent locations in the previous frame, which does not apply to highly dynamic frames or dynamic viewports. This work introduces a new concept called implicit correlation, i.e., the consistency of topological structures, which stably exists in dynamic frames and is beneficial for reducing bandwidth consumption. We design a mobile volumetric video streaming system Hermes consisting of an implicit correlation encoder to reduce bandwidth consumption and a hybrid streaming method that adapts to dynamic viewports. Experiments show that Hermes achieves a frame rate of 30+ FPS over daily networks and on commodity smartphones, with at least 3.37x improvement compared with two baselines.
|abstract =Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks. In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control. We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection. It augments the ALOHA system with a mobile base, and a whole-body teleoperation interface. Using data collected with Mobile ALOHA, we then perform supervised behavior cloning and find that co-training with existing static ALOHA datasets boosts performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling and entering an elevator, and lightly rinsing a used pan using a kitchen faucet. We will open-source all the hardware and software implementations upon publication.
|confname=MM'23
|confname =CoRL'24
|link=https://dl.acm.org/doi/pdf/10.1145/3581783.3613907
|link = https://openreview.net/forum?id=FO6tePGRZj
|title=Hermes: Leveraging Implicit Inter-Frame Correlation for Bandwidth-Efficient Mobile Volumetric Video Streaming
|title= Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation
|speaker=Mengfan
|speaker=Yi Zhou
|date=2024-06-07}}
|date=2025-12-12
}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 23:32, 11 December 2025

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

Latest

  1. [EMNLP'25] ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation, Youwei Ran
    Abstract: Code translation is a crucial activity in the software development and maintenance process, and researchers have recently begun to focus on using pre-trained large language models (LLMs) for code translation. However, existing LLMs only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code, which results in unguaranteed code executability and unreliable automated code translation. To address this issue, we propose ExeCoder, an LLM specifically designed for code translation, aimed at utilizing executability representations such as functional semantics, syntax structures, and variable dependencies to enhance the capabilities of LLMs in code translation. To evaluate the effectiveness of ExeCoder, we manually enhanced the widely used benchmark TransCoder-test, resulting in a benchmark called TransCoder-test-X that serves LLMs. Evaluation of TransCoder-test-X indicates that ExeCoder achieves state-of-the-art performance in code translation, surpassing existing open-source code LLMs by over 10.88% to 38.78% and over 27.44% to 42.97% on two metrics, and even outperforms the renowned closed-source LLM GPT-4o.
  2. [CoRL'24] Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation, Yi Zhou
    Abstract: Imitation learning from human demonstrations has shown impressive performance in robotics. However, most results focus on table-top manipulation, lacking the mobility and dexterity necessary for generally useful tasks. In this work, we develop a system for imitating mobile manipulation tasks that are bimanual and require whole-body control. We first present Mobile ALOHA, a low-cost and whole-body teleoperation system for data collection. It augments the ALOHA system with a mobile base, and a whole-body teleoperation interface. Using data collected with Mobile ALOHA, we then perform supervised behavior cloning and find that co-training with existing static ALOHA datasets boosts performance on mobile manipulation tasks. With 50 demonstrations for each task, co-training can increase success rates by up to 90%, allowing Mobile ALOHA to autonomously complete complex mobile manipulation tasks such as sauteing and serving a piece of shrimp, opening a two-door wall cabinet to store heavy cooking pots, calling and entering an elevator, and lightly rinsing a used pan using a kitchen faucet. We will open-source all the hardware and software implementations upon publication.

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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