Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-10-10 9:01'''
|time='''2025-12-12 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 = Hidden screen-camera communication techniques emerge as a new paradigm that embeds data imperceptibly into regular videos while remaining unobtrusive to human viewers. Three key goals on imperceptible, high rate, and reliable communication are desirable but conflicting, and existing solutions usually made a trade-off among them. In this paper, we present the design and implementation of ChromaCode, a screen-camera communication system that achieves all three goals simultaneously. In our design, we consider for the first time color space for perceptually uniform lightness modifications. On this basis, we design an outcome-based adaptive embedding scheme, which adapts to both pixel lightness and regional texture. Last, we propose a concatenated code scheme for robust coding and devise multiple techniques to overcome various screen-camera channel errors. Our prototype and experiments demonstrate that ChromaCode achieves remarkable raw throughputs of >700 kbps, data goodputs of 120 kbps with BER of 0.05, and with fully imperceptible flicker for viewing proved by user study, which significantly outperforms previous works.  
|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=TMC 2021
|confname =EMNLP'25
|link=https://dl.acm.org/doi/pdf/10.1145/3241539.3241543
|link = https://arxiv.org/abs/2501.18460
|title=ChromaCode: A Fully Imperceptible Screen-Camera Communication System
|title= ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation
|speaker=Mengyu}}
|speaker=Youwei Ran
|date=2025-12-12
}}
{{Latest_seminar
{{Latest_seminar
|abstract = We present MVPose, a novel system designed to enable real-time multi-person pose estimation (PE) on commodity mobile devices, which consists of three novel techniques. First, MVPose takes a motion-vector-based approach to fast and accurately track the human keypoints across consecutive frames, rather than running expensive human-detection model and pose-estimation model for every frame. Second, MVPose designs a mobile-friendly PE model that uses lightweight feature extractors and multi-stage network to significantly reduce the latency of pose estimation without compromising the model accuracy. Third, MVPose leverages the heterogeneous computing resources of both CPU and GPU to execute the pose estimation model for multiple persons in parallel, which further reduces the total latency. We present extensive experiments to evaluate the effectiveness of the proposed tecniques by implemented the MVPose on five off-the-shelf commercial smartphones. Evaluation results show that MVPose achieves over 30 frames per second PE with 4 persons per frame, which significantly outperforms the state-of-the-art baseline, with a speedup of up to 5.7 and 3.8 in latency on CPU and GPU, respectively. Compared with baseline, MVPose achieves an improvement of 10.1% in multi-person PE accuracy. Furthermore, MVPose achieves up to 74.3% and 57.6% energy-per-frame saving on average.
|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=TMC 2021
|confname =CoRL'24
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9673682
|link = https://openreview.net/forum?id=FO6tePGRZj
|title=MVPose:Realtime Multi-Person Pose Estimation using Motion Vector on Mobile Devices
|title= Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation
|speaker=Silence}}
|speaker=Yi Zhou
{{Latest_seminar
|date=2025-12-12
|abstract = Games are energy-intensive applications on mobile devices. Optimizing the energy efficiency of games is hence critical for battery-limited mobile devices. Although the advent of energy-aware scheduling (EAS) integrated in recent devices has provided opportunities for improved energy management, the framework is not specifically tuned for game applications. In this paper, we aim to improve the energy efficiency of game applications running on EAS-enabled mobile devices. To this end, we first analyze the functional characteristics of games, and investigate the source of the energy inefficiency. We then propose a scheme, called System-level Energy-optimization for Game Applications (SEGA), to improve the energy efficiency of games. SEGA governs CPU and GPU power consumption in a tightly coupled manner by employing three key techniques: (1) Lsync-aware GPU DVFS governor, (2) adaptive capacity clamping, and (3) on-demand touch boosting. We implemented SEGA on the latest Android-based smartphones. The evaluation results for 23 popular games showed that SEGA reduced the energy consumption of the Google Pixel 2 XL and Samsung Galaxy S9 Plus smartphones, at the device level, by 6.1–22.3 and 4.0–11.7 percent, respectively, with a quality of service (QoS) degradation of 1.1 and 0.5 percent, on average.
}}
|confname=TMC 2021
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9352566
|title=Optimizing Energy Consumption of Mobile Games
|speaker=Luwei}}
 
 
=== History ===
 
{{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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