Demo Data from EDBT 2026 / reviewers in the wild / expert
Feifan Gao
dblp:369/1937
· DBLP profile ↗
3ranked-venue papers
2as first author
3since 2021 · last 2026
0009-0005-3070-3377ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 |
Deep learning architectures and training · 100% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training
frequency-domain modeling |
0.9 | 1 | 2025 | AlphaPre: Amplitude-Phase Disentanglement Model for Precipitation Nowcasting · CVPR 2025 |
Image and video processing
precipitation nowcasting |
0.9 | 1 | 2025 | AlphaPre: Amplitude-Phase Disentanglement Model for Precipitation Nowcasting · CVPR 2025 |
Methods — techniques the papers use, named apart from their topics
amplitude-phase decomposition · 1.7frequency-domain learning · 0.9frequency domain learning · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | LMcast: A pretrained language model guided long-term memory transformer for precipitation nowcasting
Feifan Gao, Chuyao Luo, Guangbo Deng, Xutao Li 0003, Baoquan Zhang, Demin Yu, Yunming Ye |
Neural Networks | 1 |
| 2025 | AlphaPre: Amplitude-Phase Disentanglement Model for Precipitation NowcastingabstractPrecipitation nowcasting involves using current radar observation sequences to predict future radar sequences and determine future precipitation distribution, which is crucial for disaster warning, traffic planning, and agricultural production. Despite numerous advancements, challenges persist in accurately predicting both the location and intensity of precipitation, as these factors are often interdependent, with complex atmospheric dynamics and moisture distribution causing position and intensity changes to be intricately coupled. Inspired by the fact that in the frequency domain, phase variations are shown to correspond to changes in the position of precipitation, while amplitude variations are linked to intensity changes, we propose an amplitude-phase disentanglement model called AlphaPre, which separately learn the position and intensity changes of precipitation. AlphaPre comprises three key components: a phase network, an amplitude network, and an AlphaMixer. The phase network captures positional changes by learning phase variations, and the amplitude network models intensity changes by alternating between the frequency and spatial domains. The AlphaMixer then integrates these components to produce a refined precipitation forecast. Extensive experiments on four datasets demonstrate the effectiveness and superiority of our method over state-of-the-art approaches. Our code is publicly available at https://github.com/linkenghong/AlphaPre. Kenghong Lin, Baoquan Zhang, Demin Yu, Wenzhi Feng, Shidong Chen, Feifan Gao, Xutao Li 0003, Yunming Ye |
CVPR | 6 |
| 2024 | A 0.5-V Feedback-based CMOS Buffer with Rail-to-rail Operating RangeabstractThis paper presents a 0.5V CMOS flipped gain buffer (FGB) which is realized in 40nm CMOS technology and dedicated to very low-power operation environment. The circuit is able to provide gain function whereas its parallel architecture enables it to perform rail-to-rail operation. The employment of feedback mechanism effectively allows it to drive resistive load. The simulation results have shown that at buffer input transistor’s gm of 1µA/V and bias current of 36.54nA, the circuit can drive a minimum load of 7.74kΩ//20pF whilst offering input common-mode range of (3mV-482mV), output common-mode range of (4mV-478mV) and 10dB gain. The achieved bandwidth is 224.06kHz. The proposed buffer has demonstrated with improved performance metrics with respect to that of source follower and flipped source follower. Therefore, it is very useful for very low-voltage analog signal processing applications. Feifan Gao, Pak Kwong Chan |
ISCAS | 1 |