Microbial Decomposition
Microbial decomposition research focuses on understanding and modeling the breakdown of organic matter by microorganisms, primarily to improve predictions of nutrient cycling and carbon sequestration in various environments. Current research emphasizes developing advanced computational methods, including neural networks and decomposition-based algorithms, to analyze complex datasets and simulate decomposition processes more accurately. This work is significant for advancing ecological modeling, improving predictions of environmental change, and informing applications in areas such as bioremediation and waste management. Furthermore, decomposition techniques are being applied across diverse fields, from image analysis and natural language processing to robotics and materials science, highlighting its broad utility in data analysis and model optimization.
Papers
Convergence Bounds for Sequential Monte Carlo on Multimodal Distributions using Soft Decomposition
Holden Lee, Matheau Santana-Gijzen
DecomCAM: Advancing Beyond Saliency Maps through Decomposition and Integration
Yuguang Yang, Runtang Guo, Sheng Wu, Yimi Wang, Linlin Yang, Bo Fan, Jilong Zhong, Juan Zhang, Baochang Zhang
Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
Yang Li, Changsheng Zhao, Hyungtak Lee, Ernie Chang, Yangyang Shi, Vikas Chandra
DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation
Minzhi Li, Zhengyuan Liu, Shumin Deng, Shafiq Joty, Nancy F. Chen, Min-Yen Kan