Unravelling cell migration: defining movement from the cell surface
F Merino-Casallo, MJ Gomez-Benito… - Cell adhesion & …, 2022 - Taylor & Francis
Cell motility is essential for life and development. Unfortunately, cell migration is also linked
to several pathological processes, such as cancer metastasis. Cells' ability to migrate relies …
to several pathological processes, such as cancer metastasis. Cells' ability to migrate relies …
Transfer learning for Bayesian optimization: A survey
A wide spectrum of design and decision problems, including parameter tuning, A/B testing
and drug design, intrinsically are instances of black-box optimization. Bayesian optimization …
and drug design, intrinsically are instances of black-box optimization. Bayesian optimization …
Improved compact cuckoo search algorithm applied to location of drone logistics hub
Drone logistics can play an important role in logistics at the end of the supply chain and
special environmental logistics. At present, drone logistics is in the initial development stage …
special environmental logistics. At present, drone logistics is in the initial development stage …
Scalable inverse reinforcement learning through multifidelity Bayesian optimization
M Imani, SF Ghoreishi - IEEE transactions on neural networks …, 2021 - ieeexplore.ieee.org
Data in many practical problems are acquired according to decisions or actions made by
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
users or experts to achieve specific goals. For instance, policies in the mind of biologists …
Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic
We present a formal mathematical modeling framework for a multi-agent sequential decision
problem during an epidemic. The problem is formulated as a collaboration between a …
problem during an epidemic. The problem is formulated as a collaboration between a …
Bayesian optimization in high-dimensional spaces: A brief survey
Bayesian optimization (BO) has been widely applied to several modern science and
engineering applications such as machine learning, neural networks, robotics, aerospace …
engineering applications such as machine learning, neural networks, robotics, aerospace …
Multi-objective constrained Bayesian optimization for structural design
A Mathern, OS Steinholtz, A Sjöberg… - Structural and …, 2021 - Springer
The planning and design of buildings and civil engineering concrete structures constitutes a
complex problem subject to constraints, for instance, limit state constraints from design …
complex problem subject to constraints, for instance, limit state constraints from design …
[HTML][HTML] A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy
Y Li, S Jiao, X Gao - Defence Technology, 2021 - Elsevier
Feature extraction is an important part of signal processing, which is significant for signal
detection, classification, and recognition. The nonlinear dynamic analysis method can …
detection, classification, and recognition. The nonlinear dynamic analysis method can …
Fluctuation-based reverse dispersion entropy and its applications to signal classification
S Jiao, B Geng, Y Li, Q Zhang, Q Wang - Applied Acoustics, 2021 - Elsevier
The recently proposed fluctuation-based dispersion entropy (FDE) can distinguish various
physiological states of biomedical time series, and is usually used in the field of …
physiological states of biomedical time series, and is usually used in the field of …
Topology optimization of periodic barriers for surface waves
Dispersion engineering is always the important topic in the field of artificial periodic
structures. In particular, topology optimization of composite structures with expected …
structures. In particular, topology optimization of composite structures with expected …