Fangzhou Liu

ORCID: 0009-0008-9370-4504
Publications
Citations
Views
---
Saved
---
About
Contact & Profiles
Research Areas
  • Low-power high-performance VLSI design
  • VLSI and Analog Circuit Testing
  • VLSI and FPGA Design Techniques
  • Opinion Dynamics and Social Influence
  • Music and Audio Processing
  • Gene expression and cancer classification
  • Complex Network Analysis Techniques
  • Time Series Analysis and Forecasting
  • Machine Learning and Algorithms
  • Genetic Mapping and Diversity in Plants and Animals
  • Genetic and phenotypic traits in livestock

Chinese University of Hong Kong
2024-2025

Tianjin Academy of Agricultural Sciences
2025

Genome-wide selection (GS) represents a contemporary methodology that harnesses comprehensive array of molecular markers across the entire genome. However, challenges such as lack informative and appropriate efficient GS model(s) have confined most GS-based breeding efforts to realm laboratory simulations (Wang et al., 2023). Compared conventional prediction models, machine learning (ML) algorithm provides new insights for solving big data analysis high-performance parallel computing. using...

10.1111/pbi.14532 article EN cc-by-nc Plant Biotechnology Journal 2025-01-11

A circuit design incorporating non-integer multi-height (NIMH) cells, such as a combination of 8-track and 12-track offers increased flexibility in optimizing area, timing, power simultaneously. The conventional approach for placing NIMH cells involves using commercial tools to generate an initial global placement, followed by legalization process that divides the block area into row regions with specific heights relocates rows matching height. However, placement flow often causes...

10.1145/3626184.3633320 article EN other-oa 2024-03-12

In this work, we develop an analytical framework that integrates opinion dynamics with a recommendation system. By incorporating elements such as collaborative filtering, provide precise characterization of how systems shape interpersonal interactions and influence formation. Moreover, the property coevolution both is also shown. Specifically, convergence coevolutionary system theoretically proved, mechanisms behind filter bubble formation are elucidated. Our analysis maximum number clusters...

10.48550/arxiv.2411.11687 preprint EN arXiv (Cornell University) 2024-11-18
Coming Soon ...