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Yaxin Zhou

dblp:73/5391 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 91% Trustworthy machine learning · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › preference optimization
iterative preference optimization
0.912025
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning · ACL (1) 2025
Natural language and speech › Language models and text generation
large language model reasoning
0.912025
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning · ACL (1) 2025
Natural language and speech › Language models and text generation
preference optimization
0.912025
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning · ACL (1) 2025
Machine learning › Trustworthy machine learning
uncertainty estimation
0.312025
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning · ACL (1) 2025

Methods — techniques the papers use, named apart from their topics

uncertainty-aware preference optimization · 0.9
YearPublicationVenuePosition
2025 Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning
abstract
Lei Li, Hehuan Liu, Yaxin Zhou, ZhaoYang Gui, Xudong Weng, Yi Yuan, Zheng Wei, Zang Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
Hehuan Liu, Yaxin Zhou, ZhaoYang Gui, Xudong Weng, Zang Li
ACL (1)3
2024 Prolonging The Range of Low-power Visible Light Communication Systems with M-FSK
abstract
Visible light communication (VLC) has become a powerful and complementary technology to radio frequency (RF) communication thanks to its unrestricted bandwidth resources and high transmission rates. However, current VLC applications are limited to the short-ranged and interference-free scenarios because of the attenuation characteristics of light and the interference of ambient light. In order to extend VLC to various IoT applications, this paper tries to prolong the communication range of VLC systems based on M-frequency shift keying (MFSK), where $M$ denotes the modulation index. Specifically, 1) we modulate the luminous intensity of the LEDs to form sine waves instead of square waves and tune the sine wave frequency to transmit data. Compared to light intensity modulation, it can effectively resist the ambient light interference. Meanwhile, 2) we propose a demodulation method based on gradient thresholding to increase its fault tolerance in decoding. In addition, 3) we built a real VLC platform and conducted comprehensive experiments to verify the communication distance and reliability of the low-power VLC systems (with 1W/3W LEDs). The experimental results show that the proposed work successfully prolongs the communication distance to 5.8 meters with 1 W LED and to 10 meters with 3 W LED, extending the VLC range by nearly $\mathbf{2. 5 x}$ compared to existing work.
Mengyu Kang, Jiyi Wu, Jacheng Li, Yaxin Zhou
ICPADS6