Han J,Yang C H,Zhou X J,et al.A two-stage state transition algorithm for constrained engineering optimization problems[J].Int J of Control,Automation and Systems,2017,16(X):1-13.J. Han, C. Yang, X. Zhou, and W. Gui, "A two-stage state tran- sition algorithm for constrained ...
Recent research has focused on finding a strong, fast, and simple optimization algorithm, considering renewable energies in the electrical networks, multi-objective OPF (MOOPF) problems, and considering various objective functions to the OPF problems15. Literature review In recent literature, numerous ...
Rumors and information spreading emerge naturally from human-to-human interactions and have a growing impact on our everyday life due to increasing and faster access to information, whether trustworthy or not. A popular mathematical model for spreading r
"SCRABBLE: Single-Cell RNA-Seq Imputation Constrained by Bulk RNA-Seq Data" https://doi.org/10.1186/s13059-019-1681-8 Genome Biology 20, no. 1 (December 2019) ENHANCE, an algorithm that denoises single-cell RNA-Seq data by first performing nearest-neighbor aggregation and then inferring ...
The proposed methodology focuses on the optimisation of hyperparameters of ELM network using a single-objective Harris hawks optimisation (HHO) algorithm. The model combines the fast computational speed of ELMs with the accuracy gained by optimising hyperparameters using HHO. HHO, being a population-...
Given that our proposed optimization is non-convex and NP-hard, we develop a fast greedy algorithm whose complexity is linear in the length of the videos and the number of states of the dynamic model, hence, scales to large datasets. Under appropriate conditions on the transition model, our ...
Deep Q-Learning (DQN) [35] is a popular algorithm in Reinforcement learning, since the combination of neural networks and Q-learning can perform well in cases where the state space becomes too large [36]. DQN is an off-line algorithm, which extracts the optimal policy π by learning which...
Most methods are two-step iterative solutions, e.g., the nested fixed point (NFXP) algorithm (Rust, 1987), and the nested pseudo likelihood (NPL) algorithm (Aguirregabiria and Mira, 2002). Recently, Su and Judd (2012) proposed a constrained optimization method for structural estimation, ...
In this cross-constrained hierarchical clustering algorithm based on traditional voxel clustering, the 3D point cloud data is reduced to N 2D point cloud projection data, and each 2D point cloud data is clustered. If point cloud clusters exist in all slices at the same horizontal position, these...
(4) reconfiguration of TAD hierarchy through DNA damage, repairing, and cell apoptosis; (5) relationship between TAD hierarchy and phase separation; (6) analysis of TAD hierarchy in single-cell data; (7) algorithm improvement combined with artificial intelligence and integrated analysis with multi-...