Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2024-03-22 10:30-12:00'''
|time='''2025-04-11 10:30-12:00'''
|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=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.
|abstract = 在AI革命汹涌来袭的当下连续创业者如何实现底层认知的进化？AI对技术的影响又如何影响到企业决策？报告人何仲潇系云起老和科技有限公司创始人/CEO，四川浙大校友会理事，浙大企业导师，成都市金熊猫B类人才。让我们跟随云起老和的视角感受AI浪潮中的创业进化历程！
|confname=INFOCOM'23
|confname = 创新创业分享会
|link=https://ieeexplore.ieee.org/document/10229104
|link = https://mobinets.cn/site/Resource:Seminar
|title=Achieving Resilient and Performance-Guaranteed Routing in Space-Terrestrial Integrated Networks
|title= AI革命浪潮中的进化--连续创业者的底层认知进化与创业选择
|speaker=Luwei
|speaker= 何仲潇
|date=2024-03-29}}
|date=2025-04-11
{{Latest_seminar
}}
|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
|link=https://ieeexplore.ieee.org/document/10229043
|title=Communication-aware DNN pruning
|speaker=Shuhong
|date=2024-03-29}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 09:27, 11 April 2025

Time: 2025-04-11 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [创新创业分享会] AI革命浪潮中的进化--连续创业者的底层认知进化与创业选择, 何仲潇
    Abstract: 在AI革命汹涌来袭的当下连续创业者如何实现底层认知的进化？AI对技术的影响又如何影响到企业决策？报告人何仲潇系云起老和科技有限公司创始人/CEO，四川浙大校友会理事，浙大企业导师，成都市金熊猫B类人才。让我们跟随云起老和的视角感受AI浪潮中的创业进化历程！

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

Instructions

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