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Modelling Cell`s State in Different Cell Types
, M.Sc. Thesis Sharif University of Technology ; Hossein Khalaj, Babak (Supervisor) ; Motahari, Abolfazl (Co-Supervisor)
Abstract
Existence of heterogeneity in vital tissues of complex multicellular organisms like mammals, and fatal tissues like cancer on one hand, and limited access to biological properties of their components on the other hand, turn the study of these tissue traits to one of the most interesting fields in bioinformatics. One of the hottest subjects in this field is the recognition of functional components of these tissues by using bulk data extracted from the whole tissue.Almost every method that aims to achieve such a purpose, particularly using gene expression data, assumes that all of the cell types which constitute the studied tissue have a deterministic expression profile.In this thesis we...
A superlinearly convergent nonmonotone quasi-Newton method for unconstrained multiobjective optimization
, Article Optimization Methods and Software ; Volume 35, Issue 6 , March , 2020 , Pages 1223-1247 ; Salehi Sadaghiani, F ; Sharif University of Technology
Taylor and Francis Ltd
2020
Abstract
We propose and analyse a nonmonotone quasi-Newton algorithm for unconstrained strongly convex multiobjective optimization. In our method, we allow for the decrease of a convex combination of recent function values. We establish the global convergence and local superlinear rate of convergence under reasonable assumptions. We implement our scheme in the context of BFGS quasi-Newton method for solving unconstrained multiobjective optimization problems. Our numerical results show that the nonmonotone quasi-Newton algorithm uses fewer function evaluations than the monotone quasi-Newton algorithm. © 2020 Informa UK Limited, trading as Taylor & Francis Group
Novel adaptive Kalman filtering and fuzzy track fusion approach for real time applications
, Article 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008, Singapore, 3 June 2008 through 5 June 2008 ; 2008 , Pages 120-125 ; 9781424417186 (ISBN) ; Sadati, N ; Sharif University of Technology
2008
Abstract
The track fusion combines individual tracks formed by different sensors. Tracks are usually obtained by Kalman Filter (KF), since it is suitable for real-time application. The KF is an optimal linear estimator when the measurement noise has a Gaussian distribution with known covariance. However, in practice, some of the sensors do not have these properties, and the traditional KF is not an optimal estimator. In this paper, a novel adaptive Kalman filter (NAKF) is proposed. In this approach, the measurement noise covariance is adjusted by using an introduced simple mathematical function of one variable, called the degree of matching (DoM), where it is defined on the basis of covariance...