Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-04-06 9:30'''
|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 =Low Power Wide Area Networks (LPWANs) have been shown promising in connecting large-scale low-cost devices with low-power long-distance communication. However, existing LPWANs cannot work well for real deployments due to se�vere packet collisions. We propose OrthoRa, a new technology which significantly improves the concurrency for low-power long�distance LPWAN transmission. The key of OrthoRa is a novel design, Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which enables orthogonal packet transmissions while providing low SNR communication in LPWANs. Different nodes can send packets encoded with different orthogonal scatter chirps, and the receiver can decode collided packets from different nodes. We theoretically prove that OrthoRa provides very high concurrency for low SNR communication under different scenarios. For real networks, we address practical challenges of multiple-packet detection for collided packets, scatter chirp identification for decoding each packet and accurate packet synchronization with Carrier Frequency Offset. We implement OrthoRa on HackRF One and extensively evaluate its performance. The evaluation results show that OrthoRa improves the network throughput and concurrency by 50⇥ compared with LoRa.
|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=INFOCOM 2023
|confname =EMNLP'25
|link=https://www.jianguoyun.com/p/DaSn-A0Q_LXjBxjS9f8EIAA
|link = https://arxiv.org/abs/2501.18460
|title=Push the Limit of LPWANs with Concurrent Transmissions
|title= ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation
|speaker=Wenliang}}
|speaker=Youwei Ran
|date=2025-12-12
}}
{{Latest_seminar
{{Latest_seminar
|abstract = Mobile edge computing is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers. However, within small-cell networks, the user mobilities can result in uneven spatio-temporal loads, which have not been well studied by considering adaptive load balancing, thus limiting the system performance. Motivated by the data analytics and observations on a real-world user association dataset in a large-scale WiFi system, in this paper, we investigate the mobility-aware online task offloading problem with adaptive load balancing to minimize the total computation costs. However, the problem is intractable directly without prior knowledge of future user mobility behaviors and spatio-temporal computation loads of edge servers. To tackle this challenge, we transform and decompose the original task offloading optimization problem into two sub-problems, i.e., task offloading control ( ToC ) and server grouping ( SeG ). Then, we devise an online control scheme, named MOTO (i.e., M obility-aware O nline T ask O ffloading), which consists of two components, i.e., Long Short Term Memory based algorithm and Dueling Double DQN based algorithm, to efficiently solve the ToC and SeG sub-problems, respectively. Extensive trace-driven experiments are carried out and the results demonstrate the effectiveness of MOTO in reducing computational costs of mobile devices and achieving load balancing when compared to the state-of-the-art benchmarks.
|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 2022
|confname =CoRL'24
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9942345
|link = https://openreview.net/forum?id=FO6tePGRZj
|title=MOTO: Mobility-Aware Online Task Offloading with Adaptive Load Balancing in Small-Cell MEC
|title= Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation
|speaker=Xianyang}}
|speaker=Yi Zhou
{{Latest_seminar
|date=2025-12-12
|abstract = Edge computing capabilities in 5G wireless networks promise to benefit mobile users: computing tasks can be offloaded from user devices to nearby edge servers, reducing users’ experienced latencies. Few works have addressed how this offloading should handle long-term user mobility: as devices move, they will need to offload to different edge servers, which may require migrating data or state information from one edge server to another. In this paper, we introduce MoDEMS, a system model and architecture that provides a rigorous theoretical framework and studies the challenges of such migrations to minimize the service provider cost and user latency. We show that this cost minimization problem can be expressed as an integer linear programming problem, which is hard to solve due to resource constraints at the servers and unknown user mobility patterns. We show that finding the optimal migration plan is in general NP-hard, and we propose alternative heuristic solution algorithms that perform well in both theory and practice. We finally validate our results with real user mobility traces, ns-3 simulations, and an LTE testbed experiment. Migrations reduce the latency experienced by users of edge applications by 33% compared to previously proposed migration approaches.
}}
|confname=INFOCOM 2022
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796680
|title=MoDEMS: Optimizing Edge Computing Migrations For User Mobility
|speaker=Zhenguo}}
 
 
 
=== 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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