Design and fabrication of robust broadband extreme ultraviolet multilayers

The random layer thickness variations can induce a great deformation of the experimental reflection of broadband extreme ultraviolet multilayer. In order to reduce this influence of random layer thickness fluctuations, the multiobjective genetic algorithm has been improved and used in the robust design of multilayer with a broad angular bandpass. The robust multilayer with a lower sensitivity to random thickness errors have been obtained and the corresponding multilayer mirrors were fabricated. The experimental results of robust Mo/Si multilayer with a wide angular band were presented and analyzed, and the advantage of robust multilayer design was demonstrated

Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition

Distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models. This work studies the use of submodular functions to design a rank aggregation on score-based permutations, which can be used for distributed ASR systems in both supervised and unsupervised modes. Specifically, we compose an aggregation rank function based on the Lovasz Bregman divergence for setting up linear structured convex and nested structured concave functions. The algorithm is based on stochastic gradient descent (SGD) and can obtain well-trained aggregation models. Our experiments on the distributed ASR system show that the submodular rank aggregation can obtain higher speech recognition accuracy than traditional aggregation methods like Adaboost. Code is available online~\footnote{https://github.com/uwjunqi/Subrank}.Comment: Accepted to ICASSP 2020. Please download the pdf to view Figure 1. arXiv admin note: substantial text overlap with arXiv:1707.0116

Hydrodynamic Theories for Flows of Active Liquid Crystals and the Generalized Onsager Principle

We articulate and apply the generalized Onsager principle to derive transport equations for active liquid crystals in a fixed domain as well as in a free surface domain adjacent to a passive fluid matrix. The Onsager principle ensures fundamental variational structure of the models as well as dissipative properties of the passive component in the models, irrespective of the choice of scale (kinetic to continuum) and of the physical potentials. Many popular models for passive and active liquid crystals in a fixed domain subject to consistent boundary conditions at solid walls, as well as active liquid crystals in a free surface domain with consistent transport equations along the free boundaries, can be systematically derived from the generalized Onsager principle. The dynamical boundary conditions are shown to reduce to the static boundary conditions for passive liquid crystals used previously

Constrained Clustering Based on the Link Structure of a Directed Graph

In many segmentation applications, data objects are often clustered based purely on attribute-level similarities. This practice has neglected the useful information that resides in the link structure among data objects and the valuable expert domain knowledge about the desirable cluster assignment. Link structure can carry worthy information about the similarity between data objects (e.g. citation), and we should also incorporate the existing domain information on preferred outcome when segmenting data. In this paper, we investigate the segmentation problem combining these three sources of information, which has not been addressed in the existing literature. We propose a segmentation method for directed graphs that incorporates the attribute values, link structure and expert domain information (represented as constraints). The proposed method combines these three types of information to achieve good quality segmentation on data which can be represented as a directed graph. We conducted comprehensive experiments to evaluate various aspects of our approach and demonstrate the effectiveness of our method