Taylor series in Hermitean Clifford analysis

In this paper, we consider the Taylor decomposition for h-monogenic functions in Hermitean Clifford analysis. The latter is to be considered as a refinement of the classical orthogonal function theory, in which the structure group underlying the equations is reduced from so(2m) to the unitary Lie algebra u(m)

Electric-field-induced stress relaxation in alpha-phase poly(vinylidene fluoride) films

The relationship between elastic fatigue and electrical cyclic loading in alpha-phase poly(vinylidene fluoride) films has been investigated. Our experimental studies have shown that the electric-field-induced fatigue behavior can be described by a stress relaxation, which belongs to the Kohlrausch function group, and the corresponding exponent is a modified two-parameter Weibull distribution function.Comment: 3 pages, 3 figure

Branching Bisimilarity Checking for PRS

Recent studies reveal that branching bisimilarity is decidable for both nBPP (normed Basic Parallel Process) and nBPA (normed Basic Process Algebra). These results lead to the question if there are any other models in the hierarchy of PRS (Process Rewrite System) whose branching bisimilarity is decidable. It is shown in this paper that the branching bisimilarity for both nOCN (normed One Counter Net) and nPA (normed Process Algebra) is undecidable. These results essentially imply that the question has a negative answer.Comment: 18 pages, 6 Figure

Analyzing the impact of storage shortage on data availability in decentralized online social networks

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today’s online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction

A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data

Differential phase-contrast computed tomography (DPC-CT) is a powerful analysis tool for soft-tissue and low-atomic-number samples. Limited by the implementation conditions, DPC-CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not easy to deal with incomplete data. They are usually involved with complicated parameter selection operations, also sensitive to noise and time-consuming. In this paper, we reported a new deep learning reconstruction framework for incomplete data DPC-CT. It is the tight coupling of the deep learning neural network and DPC-CT reconstruction algorithm in the phase-contrast projection sinogram domain. The estimated result is the complete phase-contrast projection sinogram not the artifacts caused by the incomplete data. After training, this framework is determined and can reconstruct the final DPC-CT images for a given incomplete phase-contrast projection sinogram. Taking the sparse-view DPC-CT as an example, this framework has been validated and demonstrated with synthetic and experimental data sets. Embedded with DPC-CT reconstruction, this framework naturally encapsulates the physical imaging model of DPC-CT systems and is easy to be extended to deal with other challengs. This work is helpful to push the application of the state-of-the-art deep learning theory in the field of DPC-CT