Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-5-23 10:30'''
|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 = As intelligence is moving from data centers to the edges, intelligent edge devices such as smartphones, drones, robots, and smart IoT devices are equipped with the capability to altogether train a deep learning model on the devices from the data collected by themselves. Despite its considerable value, the key bottleneck of making on-device distributed training practically useful in realworld deployments is that they consume a significant amount of training time under wireless networks with constrained bandwidth. To tackle this critical bottleneck, we present Mercury, an importance sampling-based framework that enhances the training efficiency of on-device distributed training without compromising the accuracies of the trained models. The key idea behind the design of Mercury is to focus on samples that provide more important information in each training iteration. In doing this, the training efficiency of each iteration is improved. As such, the total number of iterations can be considerably reduced so as to speed up the overall training process. We implemented Mercury and deployed it on a self-developed testbed. We demonstrate its effectiveness and show that Mercury consistently outperforms two status quo frameworks on six commonly used datasets across tasks in image classification, speech recognition, and natural language processing.  
|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= SenSys 2021
|confname =EMNLP'25
|link=https://www.egr.msu.edu/~mizhang/papers/2021_SenSys_Mercury.pdf
|link = https://arxiv.org/abs/2501.18460
|title=Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling
|title= ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation
|speaker=Jiajun
|speaker=Youwei Ran
|date=2025-12-12
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract = Many datacenters and clouds manage storage systems separately from computing services for better manageability and resource utilization. These existing disaggregated storage systems use hard disks or SSDs as storage media. Recently, the technology of persistent memory (PM) has matured and seen initial adoption in several datacenters. Disaggregating PM could enjoy the same benefits of traditional disaggregated storage systems, but it requires new designs because of its memory-like performance and byte addressability. In this paper, we explore the design of disaggregating PM and managing them remotely from compute servers, a model we call passive disaggregated persistent memory, or pDPM. Compared to the alternative of managing PM at storage servers, pDPM significantly lowers monetary and energy costs and avoids scalability bottlenecks at storage servers. We built three key-value store systems using the pDPM model. The first one lets all compute nodes directly access and manage storage nodes. The second uses a central coordinator to orchestrate the communication between compute and storage nodes. These two systems have various performance and scalability limitations. To solve these problems, we built Clover, a pDPM system that separates the location, communication mechanism, and management strategy of the data plane and the metadata/control plane. Compute nodes access storage nodes directly for data operations, while one or few global metadata servers handle all metadata/control operations. From our extensive evaluation of the three pDPM systems, we found Clover to be the best-performing pDPM system. Its performance under common datacenter workloads is similar to non-pDPM remote in-memory key-value store, while reducing CapEx and OpEx by 1.4× and 3.9×.
|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= ATC 2020
|confname =CoRL'24
|link=https://www.usenix.org/system/files/atc20-tsai.pdf
|link = https://openreview.net/forum?id=FO6tePGRZj
|title=Disaggregating Persistent Memory and Controlling Them Remotely: An Exploration of Passive Disaggregated Key-Value Stores
|title= Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation
|speaker=Qinyong
|speaker=Yi Zhou
|date=2025-12-12
}}
}}
=== 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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