Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2025-12-12 10:30'''
|time='''2025-12-20 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]].
Line 8: Line 8:


{{Latest_seminar
{{Latest_seminar
|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.  
|abstract = Low Earth Orbit (LEO) satellite networks are expected to enable global connectivity for next-generation communications. To provide space-centric solutions, the limited coverage time and limited resources of LEO satellites pose challenges to maintaining service continuity and ensuring low latency for users. Furthermore, LEO satellites rely on solar panels to obtain energy, so a balance needs to be struck between energy consumption and service provision for satellite mobile edge computing. In this paper, we aim to achieve space-centric computational task offloading in LEO satellite networks. The goal is to minimize end-to-end task offloading latency while considering the constraints posed by the limited onboard computing, storage, and energy resources in constantly moving LEO satellites. To achieve this, we formulate a joint problem of service migration and power control in energy-harvesting LEO satellite networks. The problem is then converted into a Markov decision process (MDP) and solved with SpaceEdge, a novel algorithm based on Deep Reinforcement Learning (DRL). SpaceEdge offers supports for both centralized learning and multi-agent learning. Simulation results show that SpaceEdge, particularly the multi-agent model, outperforms existing solutions, demonstrating its effectiveness in deploying space-centric task offloading services in LEO satellite networks.
|confname =EMNLP'25
|confname =TWC'24
|link = https://arxiv.org/abs/2501.18460
|link = https://ieeexplore.ieee.org/abstract/document/10623400
|title= ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation
|title= SpaceEdge: Optimizing Service Latency and Sustainability for Space-Centric Task Offloading in LEO Satellite Networks
|speaker=Youwei Ran
|speaker=Haifeng
|date=2025-12-12
|date=2025-12-20
}}
}}
{{Latest_seminar
{{Latest_seminar
|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.
|abstract =For highly immersive mobile volumetric video streaming, it is essential to deliver photo-realistic full-scene content with smooth playback. Unlike traditional representations such as point clouds, 3D Gaussian Splatting (3DGS) has gained attention for its ability to represent high-quality full-scene 3D content. However, our preliminary experiments show that existing methods for 3DGS-based videos fail to achieve smooth playback on mobile devices. In this paper, we propose Vega, a 3DGS-based photo-realistic full-scene volumetric video streaming system that ensures real-time playback on mobile devices. The core idea behind Vega's real-time rendering is object-level selective computation, which allocates computational resources to visually important objects to meet strict rendering deadlines. To enable mobile streaming based on the selective computation, Vega addresses two challenges: (1) designing an encoding scheme that optimizes the data size of videos while being compatible with object-level prioritization, and (2) developing a rendering pipeline that efficiently operates on resource-constrained mobile devices. We implemented an end-to-end Vega system, consisting of a streaming server and an Android application. Experimental results on commodity smartphones show that Vega achieves 30 frames per second (FPS) for full-scene volumetric video streaming while maintaining competitive data size and visual quality compared to existing baselines.
|confname =CoRL'24
|confname =Mobicom'25
|link = https://openreview.net/forum?id=FO6tePGRZj
|link = https://dl.acm.org/doi/10.1145/3680207.3765267
|title= Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation
|title= Vega: Fully Immersive Mobile Volumetric Video Streaming with 3D Gaussian Splatting
|speaker=Yi Zhou
|speaker=Jiyi
|date=2025-12-12
|date=2025-12-20
}}
}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 01:26, 19 December 2025

Time: 2025-12-20 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [TWC'24] SpaceEdge: Optimizing Service Latency and Sustainability for Space-Centric Task Offloading in LEO Satellite Networks, Haifeng
    Abstract: Low Earth Orbit (LEO) satellite networks are expected to enable global connectivity for next-generation communications. To provide space-centric solutions, the limited coverage time and limited resources of LEO satellites pose challenges to maintaining service continuity and ensuring low latency for users. Furthermore, LEO satellites rely on solar panels to obtain energy, so a balance needs to be struck between energy consumption and service provision for satellite mobile edge computing. In this paper, we aim to achieve space-centric computational task offloading in LEO satellite networks. The goal is to minimize end-to-end task offloading latency while considering the constraints posed by the limited onboard computing, storage, and energy resources in constantly moving LEO satellites. To achieve this, we formulate a joint problem of service migration and power control in energy-harvesting LEO satellite networks. The problem is then converted into a Markov decision process (MDP) and solved with SpaceEdge, a novel algorithm based on Deep Reinforcement Learning (DRL). SpaceEdge offers supports for both centralized learning and multi-agent learning. Simulation results show that SpaceEdge, particularly the multi-agent model, outperforms existing solutions, demonstrating its effectiveness in deploying space-centric task offloading services in LEO satellite networks.
  2. [Mobicom'25] Vega: Fully Immersive Mobile Volumetric Video Streaming with 3D Gaussian Splatting, Jiyi
    Abstract: For highly immersive mobile volumetric video streaming, it is essential to deliver photo-realistic full-scene content with smooth playback. Unlike traditional representations such as point clouds, 3D Gaussian Splatting (3DGS) has gained attention for its ability to represent high-quality full-scene 3D content. However, our preliminary experiments show that existing methods for 3DGS-based videos fail to achieve smooth playback on mobile devices. In this paper, we propose Vega, a 3DGS-based photo-realistic full-scene volumetric video streaming system that ensures real-time playback on mobile devices. The core idea behind Vega's real-time rendering is object-level selective computation, which allocates computational resources to visually important objects to meet strict rendering deadlines. To enable mobile streaming based on the selective computation, Vega addresses two challenges: (1) designing an encoding scheme that optimizes the data size of videos while being compatible with object-level prioritization, and (2) developing a rendering pipeline that efficiently operates on resource-constrained mobile devices. We implemented an end-to-end Vega system, consisting of a streaming server and an Android application. Experimental results on commodity smartphones show that Vega achieves 30 frames per second (FPS) for full-scene volumetric video streaming while maintaining competitive data size and visual quality compared to existing 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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