Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''Thursday 16:20-18:00'''
|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=Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts assume the intensity of LED light is constant. This hypothesis is not true when Smart Lighting is introduced to VLC, which requires LEDs to adapt their brightness based on the intensity of natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A novel Adaptive Multiple Pulse Position Modulation (AMPPM) scheme is proposed to support fine-grained dimming levels to avoid flickering while maximizing the throughput under each dimming level. SmartVLC is implemented on off-the-shelf commodity hardware. Several real-life challenges in both hardware and software are addressed to make it a robust real-time system. Comprehensive experiments are carried out to evaluate the system performance under multifaceted scenarios. Experimental results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40 and 12 percent on average, respectively, without bringing any flickering to users.
|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 '20
|confname =EMNLP'25
|link=https://ieeexplore.ieee.org/document/8708935
|link = https://arxiv.org/abs/2501.18460
|title=SmartVLC: Co-Designing Smart Lighting and Communication for Visible Light Networks
|title= ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation
|speaker=Mengyu
|speaker=Youwei Ran
|date=2023-11-16}}
|date=2025-12-12
}}
{{Latest_seminar
{{Latest_seminar
|abstract=In VANETs, it is important to support fast and reliable multi-hop broadcast for safety-related applications. The performance of multi-hop broadcast schemes is greatly affected by relay selection strategies. However, the relationship between the relay selection strategies and the expected broadcast performance has not been fully characterized yet. Furthermore, conventional broadcast schemes usually attempt to minimize the waiting time difference between adjacent relay candidates to reduce the waiting time overhead, which makes the relay selection process vulnerable to internal interference, occurring due to retransmissions from previous forwarders and transmissions from redundant relays. In this paper, we jointly take both of the relay selection and the internal interference mitigation into account and propose a fast, reliable, opportunistic multi-hop broadcast scheme, in which we utilize a novel metric called the expected broadcast speed in relay selection and propose a delayed retransmission mechanism to mitigate the adverse effect of retransmissions from previous forwarders and an expected redundancy probability based mechanism to mitigate the adverse effect of redundant relays. The performance evaluation results show that the proposed scheme yields the best broadcast performance among the four schemes in terms of the broadcast coverage ratio and the end-to-end delivery latency.
|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 '23
|confname =CoRL'24
|link=https://ieeexplore.ieee.org/document/9566795
|link = https://openreview.net/forum?id=FO6tePGRZj
|title=A Fast, Reliable, Opportunistic Broadcast Scheme With Mitigation of Internal Interference in VANETs
|title= Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation
|speaker=Luwei
|speaker=Yi Zhou
|date=2023-11-16}}
|date=2025-12-12
{{Latest_seminar
}}
|abstract=Deploying deep convolutional neural network (CNN) to perform video analytics at edge poses a substantial system challenge, as running CNN inference incurs a prohibitive cost in computational resources. Model partitioning, as a promising approach, splits CNNs and distributes them to multiple edge devices in closer proximity to each other for serial inferences, however, it causes considerable cross-edge delay for transmitting intermediate feature maps. To overcome this challenge, we present ResMap, a new edge video analytics framework that significantly improves the cross-edge transmission and flexibly partitions the CNNs. Briefly, by exploiting the sparsity of the intermediate raw or residual feature map, ResMap effectively removes the redundant transmission, thereby decreasing the cross-edge transmission delay. In addition, ResMap incorporates an Online Data-Aware Scheduler to regularly update the CNN partitioning scheme so as to adapt to the time-varying edge runtime and video content. We have implemented ResMap fully based on COTS hardware, and the experimental results show that ResMap reduces the intermediate feature map volume by 14.93-46.12% and improves the average processing time by 17.43-30.6% compared to other alternative designs.
|confname=INFOCOM '23
|link=https://ieeexplore.ieee.org/document/10228990
|title=ResMap: Exploiting Sparse Residual Feature Map for Accelerating Cross-Edge Video Analytics
|speaker=Xianyang
|date=2023-11-16}}
{{Latest_seminar
|abstract=Serverless applications are typically composed of function workflows in which multiple short-lived functions are triggered to exchange data in response to events or state changes. Current serverless platforms coordinate and trigger functions by following high-level invocation dependencies but are oblivious to the underlying data exchanges between functions. This design is neither efficient nor easy to use in orchestrating complex workflows – developers often have to manage complex function interactions by themselves, with customized implementation and unsatisfactory performance. In this paper, we argue that function orchestration should follow a data-centric approach. In our design, the platform provides a data bucket abstraction to hold the intermediate data generated by functions. Developers can use a rich set of data trigger primitives to control when and how the output of each function should be passed to the next functions in a workflow. By making data consumption explicit and allowing it to trigger functions and drive the workflow, complex function interactions can be easily and efficiently supported. We present Pheromone – a scalable, low-latency serverless platform following this data-centric design. Compared to well-established commercial and open-source platforms, Pheromone cuts the latencies of function interactions and data exchanges by orders of magnitude, scales to large workflows, and enables easy implementation of complex applications.
|confname=NSDI '23
|link=https://www.usenix.org/conference/nsdi23/presentation/yu
|title=Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing
|speaker=Mengfan
|date=2023-11-16}}
{{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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