Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2025-02-28 10:30-12:00'''
|time='''2025-03-14 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]].
Line 8: Line 8:


{{Latest_seminar
{{Latest_seminar
|abstract = The emerging programmable networks sparked significant research on Intelligent Network Data Plane (INDP), which achieves learning-based traffic analysis at line-speed. Prior art in INDP focus on deploying tree/forest models on the data plane. We observe a fundamental limitation in tree-based INDP approaches: although it is possible to represent even larger tree/forest tables on the data plane, the flow features that are computable on the data plane are fundamentally limited by hardware constraints. In this paper, we present BoS to push the boundaries of INDP by enabling Neural Network (NN) driven traffic analysis at line-speed. Many types of NNs (such as Recurrent Neural Network (RNN), and transformers) that are designed to work with sequential data have advantages over tree-based models, because they can take raw network data as input without complex feature computations on the fly. However, the challenge is significant: the recurrent computation scheme used in RNN inference is fundamentally different from the match-action paradigm used on the network data plane. BoS addresses this challenge by (i) designing a novel data plane friendly RNN architecture that can execute unlimited RNN time steps with limited data plane stages, effectively achieving line-speed RNN inference; and (ii) complementing the on-switch RNN model with an off-switch transformer-based traffic analysis module to further boost the overall performance. We implement a prototype of BoS using a P4 programmable switch as our data plane, and extensively evaluate it over multiple traffic analysis tasks. The results show that BoS outperforms state-of-the-art in both analysis accuracy and scalability..
|abstract = Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. This typically involves extensive sampling at inference time guided by an external LLM verifier, resulting in a two-player system. Despite external guidance, the effectiveness of this system demonstrates the potential of a single LLM to tackle complex tasks. Thus, we pose a new research problem: Can we internalize the searching capabilities to fundamentally enhance the reasoning abilities of a single LLM? This work explores an orthogonal direction focusing on post-training LLMs for autoregressive searching (i.e., an extended reasoning process with self-reflection and self-exploration of new strategies). To achieve this, we propose the Chain-of-Action-Thought (COAT) reasoning and a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning. Our approach results in Satori, a 7B LLM trained on open-source models and data. Extensive empirical evaluations demonstrate that Satori achieves state-of-the-art performance on mathematical reasoning benchmarks while exhibits strong generalization to out-of-domain tasks. Code, data, and models will be fully open-sourced.
|confname =NSDI'24
|confname = Arxiv
|link = https://www.usenix.org/conference/nsdi24/presentation/yan
|link = https://arxiv.org/abs/2502.02508
|title= Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed
|title= Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
|speaker=Youwei
|speaker=Qinyong
|date=2025-02-28
|date=2025-03-14
}}{{Latest_seminar
|abstract = Light bulbs have been recently explored to design Light Fidelity (LiFi) communication to battery-free tags, thus complementing Radiofrequency (RF) backscatter in the uplink. In this paper, we show that LiFi and RF backscatter are complementary and have unexplored interactions. We introduce PassiveLiFi, a battery-free system that uses LiFi to transmit RF backscatter at a meagre power budget. We address several challenges on the system design in the LiFi transmitter, the tag and the RF receiver. We design the first LiFi transmitter that implements a chirp spread spectrum (CSS) using the visible light spectrum. We use a small bank of solar cells for both communication and harvesting, and reconfigure them based on the amount of harvested energy and desired data rate. We further alleviate the low responsiveness of solar cells with a new low-power receiver design in the tag. We design and implement a novel technique for embedding multiple symbols in the RF backscatter based on delayed chirps. Experimental results with an RF carrier of 17dBm show that we can generate RF backscatter with a range of 92.1 meters/ μW consumed in the tag, which is almost double with respect to prior work.
|confname =ToN'23
|link = https://ieeexplore.ieee.org/document/10371205/
|title= LiFi for Low-Power and Long-Range RF Backscatter
|speaker=Mengyu
|date=2025-03-14
}}
}}


{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 09:59, 14 March 2025

Time: 2025-03-14 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [Arxiv] Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search, Qinyong
    Abstract: Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. This typically involves extensive sampling at inference time guided by an external LLM verifier, resulting in a two-player system. Despite external guidance, the effectiveness of this system demonstrates the potential of a single LLM to tackle complex tasks. Thus, we pose a new research problem: Can we internalize the searching capabilities to fundamentally enhance the reasoning abilities of a single LLM? This work explores an orthogonal direction focusing on post-training LLMs for autoregressive searching (i.e., an extended reasoning process with self-reflection and self-exploration of new strategies). To achieve this, we propose the Chain-of-Action-Thought (COAT) reasoning and a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning. Our approach results in Satori, a 7B LLM trained on open-source models and data. Extensive empirical evaluations demonstrate that Satori achieves state-of-the-art performance on mathematical reasoning benchmarks while exhibits strong generalization to out-of-domain tasks. Code, data, and models will be fully open-sourced.
  2. [ToN'23] LiFi for Low-Power and Long-Range RF Backscatter, Mengyu
    Abstract: Light bulbs have been recently explored to design Light Fidelity (LiFi) communication to battery-free tags, thus complementing Radiofrequency (RF) backscatter in the uplink. In this paper, we show that LiFi and RF backscatter are complementary and have unexplored interactions. We introduce PassiveLiFi, a battery-free system that uses LiFi to transmit RF backscatter at a meagre power budget. We address several challenges on the system design in the LiFi transmitter, the tag and the RF receiver. We design the first LiFi transmitter that implements a chirp spread spectrum (CSS) using the visible light spectrum. We use a small bank of solar cells for both communication and harvesting, and reconfigure them based on the amount of harvested energy and desired data rate. We further alleviate the low responsiveness of solar cells with a new low-power receiver design in the tag. We design and implement a novel technique for embedding multiple symbols in the RF backscatter based on delayed chirps. Experimental results with an RF carrier of 17dBm show that we can generate RF backscatter with a range of 92.1 meters/ μW consumed in the tag, which is almost double with respect to prior work.

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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