Difference between revisions of "Resource:Seminar"

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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=We develop a Multi-Agent Reinforcement Learning (MARL) method to learn scalable control policies for target tracking. Our method can handle an arbitrary number of pursuers and targets; we show results for tasks consisting up to 1000 pursuers tracking 1000 targets. We use a decentralized, partially-observable Markov Decision Process framework to model pursuers as agents receiving partial observations (range and bearing) about targets which move using fixed, unknown policies. An attention mechanism is used to parameterize the value function of the agents; this mechanism allows us to handle an arbitrary number of targets. Entropy-regularized off-policy RL methods are used to train a stochastic policy, and we discuss how it enables a hedging behavior between pursuers that leads to a weak form of cooperation in spite of completely decentralized control execution. We further develop a masking heuristic that allows training on smaller problems with few pursuers-targets and execution on much larger problems. Thorough simulation experiments and comparisons to state of the art algorithms are performed to study the scalability of the approach and robustness of performance to varying numbers of agents and targets.
|abstract=Satellite routers in emerging space-terrestrial integrated networks (STINs) are operated in a failure-prone, intermittent and resource-constrained space environment, making it very critical but challenging to cope with various network failures effectively. Existing resilient routing approaches either suffer from continuous re-convergences with low network reachability, or involve prohibitive pre-computation and storage overhead due to the huge amount of possible failure scenarios in STINs.This paper presents StarCure, a novel resilient routing mechanism for futuristic STINs. StarCure aims at achieving fast and efficient routing restoration, while maintaining the low-latency, high-bandwidth service capabilities in failure-prone space environments. First, StarCure incorporates a new network model, called the topology-stabilizing model (TSM) to eliminate topological uncertainty by converting the topology variations caused by various failures to traffic variations. Second, StarCure adopts an adaptive hybrid routing scheme, collaboratively combining a constraint optimizer to efficiently handle predictable failures, together with a location-guided protection routing strategy to quickly deal with unexpected failures. Extensive evaluations driven by realistic constellation information show that, StarCure can protect routing against various failures, achieving close-to-100% reachability and better performance restoration with acceptable system overhead, as compared to other existing resilience solutions.
|confname=IROS 2021
|confname=INFOCOM'23
|link=https://ieeexplore.ieee.org/abstract/document/9636344
|link=https://ieeexplore.ieee.org/document/10229104
|title=Scalable Reinforcement Learning Policies for Multi-Agent Control
|title=Achieving Resilient and Performance-Guaranteed Routing in Space-Terrestrial Integrated Networks
|speaker=Xianyang
|speaker=Luwei
|date=2024-03-22}}
|date=2024-03-29}}
{{Latest_seminar
{{Latest_seminar
|abstract=With the popularity of LED infrastructure and the camera on smartphone, LED-Camera visible light communication (VLC) has become a realistic and promising technology. However, the existing LED-Camera VLC has limited throughput due to the sampling manner of camera. In this paper, by introducing a polarization dimension, we propose a hybrid modulation scheme with LED and polarization signals to boost throughput. Nevertheless, directly mixing LED and polarized signals may suffer from channel conflict. We exploit well-designed packet structure and Symmetric Return-to-Zero Inverted (SRZI) coding to overcome the conflict. In addition, in the demodulation of hybrid signal, we alleviate the noise caused by polarization on the LED signals by polarization background subtraction. We further propose a pixel-free approach to correct the perspective distortion caused by the shift of view angle by adding polarizers around the liquid crystal array. We build a prototype of this hybrid modulation scheme using off-the-shelf optical components. Extensive experimental results demonstrate that the hybrid modulation scheme can achieve reliable communication, achieving 13.4 kbps throughput, which is 400 % of the existing state-of-the-art LED-Camera VLC.
|abstract=We propose a Communication-aware Pruning (CaP) algorithm, a novel distributed inference framework for distributing DNN computations across a physical network. Departing from conventional pruning methods, CaP takes the physical network topology into consideration and produces DNNs that are communication-aware, designed for both accurate and fast execution over such a distributed deployment. Our experiments on CIFAR-10 and CIFAR-100, two deep learning benchmark datasets, show that CaP beats state of the art competitors by up to 4% w.r.t. accuracy on benchmarks. On experiments over real-world scenarios, it simultaneously reduces total execution time by 27%–68% at negligible performance decrease (less than 1%).
|confname=INFOCOM 2023
|confname=INFOCOM 2023
|link=https://ieeexplore.ieee.org/document/10228936/
|link=https://ieeexplore.ieee.org/document/10229043
|title=Breaking the Throughput Limit of LED-Camera Communication via Superposed Polarization
|title=Communication-aware DNN pruning
|speaker=Mengyu
|speaker=Shuhong
|date=2024-03-22}}
|date=2024-03-29}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 14:03, 26 March 2024

Time: 2024-03-22 10:30-12:00
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [INFOCOM'23] Achieving Resilient and Performance-Guaranteed Routing in Space-Terrestrial Integrated Networks, Luwei
    Abstract: Satellite routers in emerging space-terrestrial integrated networks (STINs) are operated in a failure-prone, intermittent and resource-constrained space environment, making it very critical but challenging to cope with various network failures effectively. Existing resilient routing approaches either suffer from continuous re-convergences with low network reachability, or involve prohibitive pre-computation and storage overhead due to the huge amount of possible failure scenarios in STINs.This paper presents StarCure, a novel resilient routing mechanism for futuristic STINs. StarCure aims at achieving fast and efficient routing restoration, while maintaining the low-latency, high-bandwidth service capabilities in failure-prone space environments. First, StarCure incorporates a new network model, called the topology-stabilizing model (TSM) to eliminate topological uncertainty by converting the topology variations caused by various failures to traffic variations. Second, StarCure adopts an adaptive hybrid routing scheme, collaboratively combining a constraint optimizer to efficiently handle predictable failures, together with a location-guided protection routing strategy to quickly deal with unexpected failures. Extensive evaluations driven by realistic constellation information show that, StarCure can protect routing against various failures, achieving close-to-100% reachability and better performance restoration with acceptable system overhead, as compared to other existing resilience solutions.
  2. [INFOCOM 2023] Communication-aware DNN pruning, Shuhong
    Abstract: We propose a Communication-aware Pruning (CaP) algorithm, a novel distributed inference framework for distributing DNN computations across a physical network. Departing from conventional pruning methods, CaP takes the physical network topology into consideration and produces DNNs that are communication-aware, designed for both accurate and fast execution over such a distributed deployment. Our experiments on CIFAR-10 and CIFAR-100, two deep learning benchmark datasets, show that CaP beats state of the art competitors by up to 4% w.r.t. accuracy on benchmarks. On experiments over real-world scenarios, it simultaneously reduces total execution time by 27%–68% at negligible performance decrease (less than 1%).

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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