Transfer learning in deep neural network based under-sampled MR image reconstruction

In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data. In this research, a transfer learning approach i.e. end-to-end fine tuning is proposed for U-Net to address the data scarcity and generalization problems of deep learning-based MR image reconstruction. First the generalization capabilities of a pre-trained U-Net (initially trained on the human brain images of 1.5 T scanner) are assessed for: (a) MR images acquired from MRI scanners of different magnetic field strengths, (b) MR images of different anatomies and (c) MR images under-sampled by different acceleration factors. Later, end-to-end fine tuning of the pre-trained U-Net is proposed for the reconstruction of the above-mentioned MR images (i.e. (a), (b) and (c)). The results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images.     

TFSI钝化晶体硅表面性质的第一性原理研究

晶体硅表面钝化是高效率晶体硅太阳能电池的核心技术,直接影响晶体硅器件的性能。本文采用第一性原理方法研究了一种超强酸-双三氟甲基磺酰亚胺(TFSI)钝化晶体硅(001)表面。研究发现,TFSI的四氧原子结构能够与Si(001)表面Si原子有效成键,吸附能达到-5.124 eV。电子局域函数研究表明,TFSI的O原子与晶体硅表面的Si的成键类型为金属键。由态密度和电荷差分密度分析可知,Si表面原子的电子向TFSI转移,从而有效降低了Si表面的电子复合中心,有利于提高晶体硅的少子寿命。Bader电荷显示,伴随着TFSI钝化晶体硅表面的Si原子,表面Si原子电荷电量减少,而TFSI中的O原子和S原子电荷电量相应增加,进一步证明了TFSI钝化Si表面后的电子转移。该工作为第一性原理方法预测有机强酸钝化晶体硅表面的钝化效果提供了数据支撑。     

Multifold factorizations of cyclic groups into subsets

Let $(G,+)$ be an abelian group. A finite multiset A over G is said to give a λ-fold factorization of G if there exists a multiset B over G such that each element of G occurs λ times in the multiset $A+B:=\{a+b:a\in A,b\in B\}$. In this article, restricting G to a cyclic group, we will provide sufficient conditions on a given multiset A under which the exact value or an upper bound of the minimum multiplicity λ of a factorization of G can be given by introducing a concept of ‘lcm-closure’. Furthermore, a couple of properties on a given factor A will be shown when A has a prime or prime power order (cardinality). A relation to multifold factorizations of the set of integers will be also glanced at a general perspective.     

A linear time algorithm for balance vertices on trees

The concept of balance vertices was first investigated by Reid (1999). For the main result “the balance vertices of a tree consist of a single vertex or two adjacent vertices”, Shan and Kang (2004) and Reid and DePalma (2005) improved the length and technique of the proof. In this paper we further discuss the balance vertices on trees in a generalization context. We do not only provide a simple efficient proof for the relevant result but also develop a linear time algorithm to find the set of balance vertices on the underlying tree.     

Generation of strongly non-Gaussian stochastic processes by iterative scheme upgrading phase and amplitude contents

Random excitations, such as wind velocity, always exhibit non-Gaussian features. Sample realisations of stochastic processes satisfying given features should be generated, in order to perform the dynamical analysis of structures under stochastic loads based on the Monte Carlo simulation. In this paper, an efficient method is proposed to generate stationary non-Gaussian stochastic processes. It involves an iterative scheme that produces a class of sample processes satisfying the following conditions. (1) The marginal cumulative distribution function of each sample process is perfectly identical to the prescribed one. (2) The ensemble-averaged power spectral density function of these non-Gaussian sample processes is as close to the prescribed target as possible. In this iterative scheme, the underlying processes are generated by means of the spectral representation method that recombines the upgraded power spectral density function with the phase contents of the new non-Gaussian processes in the latest iteration. Numerical examples are provided to demonstrate the capabilities of the proposed approach for four typical non-Gaussian distributions, some of which deviate significantly from the Gaussian distribution. It is found that the estimated power spectral density functions of non-Gaussian processes are close to the target ones, even for the extremely non-Gaussian case. Furthermore, the capability of the proposed method is compared to two other methods. The results show that the proposed method performs well with convergence speed, accuracy, and random errors of power spectral density functions.     

Experimental validation of internal model approach for tracking control of a MEMS micromirror without angular velocity measurement

Nonlinear Dynamics - This paper addresses the angular tracking control for an electromagnetic MEMS micromirror. The problem is formulated in the output regulation framework for output feedback...     

STABILITY AND BIFURCATION OF A STAGE-STRUCTURED PREDATOR-PREY MODEL INCORPORATING A CONSTANT PREY REFUGE

A stage-structured predator-prey model incorporating a constant prey refuge is proposed in this paper. The stability analysis of the equilibria is carried out. We also study Hopf bifurcations occurring at the positive equilibrium by choosing a suitable time delay as bifurcating parameter.