Advanced Topics in AI for Computing and Networking (Winter 2026/2027)

2026-10-22T15:26:20Z

Wenfang.wu: /* Schedule */

== Details ==

{{CourseDetails
|credits=5 ECTS
|module=M.Inf.1123
|lecturer=[http://134.76.18.81/?q=people/prof-dr-xiaoming-fu Prof. Xiaoming Fu];
|ta=Wenfang Wu; Hao Xu
|time=Thursday 14:00-16:00
|place=
}}

==Announcements==
Please contact me by email: wenfang.wu@cs.uni-goettingen.de if you have any questions.

==Course Overview==
The purpose of this seminar is to discuss some advanced topics in '''computer networks'''. This course is a '''theory-oriented''' research seminar (5 ECTS, 2 SWS), held on a weekly base and comprises the following components:
* Weekly Presentation + Weekly Paper Reading and Discussion 40%
* Final Presentation 40%
* Final Report 20%

The material in the seminar is mainly drawn from the research literature in top journals/conferences, like ToN,TMC, TPDS, SIGCOMM, SIGMETRICS, INFOCOM, MOBICOM, MOBIHOC, WWW, CoNEXT.

==Requirements==
* Each participant is required to read the assigned paper before the seminar and prepare the review of the paper, which should include the following parts:
** Summary of the paper
** Pros and cons of the paper (your conclusion)
** '''NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 on Wedesday). => the review form is available at [[http://user.informatik.uni-goettingen.de/~fu/Paper_Review_Form_ATCN_WS201112.doc Paper_Review_Form_ATCN_WS201112.doc]]
* During each weekly seminar, one participant is assigned for presenting the paper (each presentation lasts for ~20 minutes) and the list of pros and cons are discussed by all the participants.
* In the middle of the semester, everyone is requested to prepare:
** Final report: Essay (5~6 pages, double columns, IEEE format) for your chosen research topic, which contains a comprehensive literature survey + a detailed discussion of some key enabling technologies
** Final presentation: each presentation lasts for ~20 minutes, plus ~10 minutes Q&A

==List of Papers ==
1. DSV-LFS: Unifying LLM-Driven Semantic Cues with Visual Features for Robust Few-Shot Segmentation [https://arxiv.org/pdf/2503.04006]

2. STPro: Spatial and Temporal Progressive Learning for Weakly Supervised Spatio-Temporal Grounding [https://arxiv.org/pdf/2502.20678]

3. Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation [https://arxiv.org/pdf/2503.13068]

4. WeGen: A Unified Model for Interactive Multimodal Generation as We Chat [https://arxiv.org/pdf/2503.01115]

5. M3-VOS: Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation [https://arxiv.org/pdf/2412.13803]

6. BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices [https://arxiv.org/pdf/2411.11909]

7. SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization [https://ieeexplore.ieee.org/document/10228858]

8. Energy-Efficient 360-Degree Video Streaming on Multicore-Based Mobile Devices [https://ieeexplore.ieee.org/document/10228863]

9. Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning [https://ieeexplore.ieee.org/document/10228869]

10. WiseCam: Wisely Tuning Wireless Pan-Tilt Cameras for Cost-Effective Moving Object Tracking [https://ieeexplore.ieee.org/document/10228926]

11. Jupiter: Fast and Resource-Efficient Collaborative Inference of Generative LLMs on Edge Devices [https://arxiv.org/pdf/2504.08242]

12. Joint Optimization of Prompt Security and System Performance in Edge-Cloud LLM Systems [https://arxiv.org/pdf/2501.18663]

13. iRadar: Synthesizing Millimeter-Waves from Wearable Inertial Inputs for Human Gesture Sensing [https://arxiv.org/pdf/2412.15980]

14. Mell: Memory-Efficient Large Language Model Serving via Multi-GPU KV Cache Management [https://arxiv.org/pdf/2501.06709]

15. SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models [https://arxiv.org/pdf/2503.15921]

16. Quark: Implementing Convolutional Neural Networks Entirely on Programmable Data Plane [https://arxiv.org/pdf/2501.15100]

17. PDStream: Slashing Long-Tail Delay in Interactive Video Streaming via Pseudo-Dual Streaming [https://arxiv.org/pdf/2502.04691]

18. Understanding Human Preferences: Towards More Personalized Video to Text Generation [https://dl.acm.org/doi/10.1145/3589334.3645711]

19. Multimodal Relation Extraction via a Mixture of Hierarchical Visual Context Learners [https://dl.acm.org/doi/abs/10.1145/3589334.3645603]

20. Graph Contrastive Learning via Interventional View Generation [https://dl.acm.org/doi/pdf/10.1145/3589334.3645687]

==Schedule==
W1: Open Talk (29.10)

W2: Select papers and create schedule

W4:

W6:

W8:

W10:

W12:

W14:

'''!! xx.xx deadline for registration on Flexnow'''

Final Presentation (xx.01)
*'''Paper Title''':
*'''Paper Title''':

Report deadline

==Final Presentations & Report==

*Final Registration in '''FlexNow''': To Be Announced (TBA).

*Final Presentation:
**Each for ~20 minutes, plus ~20 minutes Q&A

*Final Presentation Slots:
**To Be Announced (TBA)

*Final Report:
**Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/)
**Due by 23:59pm 15th February.

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Advanced Topics in AI for Computing and Networking (Winter 2026/2027)