A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment

Alignment of multiple multi-relational networks, such as knowledge graphs, is vital for AI applications. Different from the conventional alignment models, we apply the graph convolutional network (GCN) to achieve more robust network embedding for the alignment task. In comparison with existing GCNs which cannot fully utilize multi-relation information, we propose a vectorized relational graph convolutional network (VR-GCN) to learn the embeddings of both graph entities and relations simultaneously for multi-relational networks. The role discrimination and translation property of knowledge graphs are adopted in the convolutional process. Thereafter, AVR-GCN, the alignment framework based on VR-GCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function which aims at minimizing the distances between anchors, and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the state-of-the-art methods in terms of network embedding, entity alignment, and relation alignment.

Some Properties of Fundamental Linear Canonical Zak Transform

In this paper, we introduce some fundamentally properties of Zak linear canonical transform (LCZT) such as linearity and translation properties. LCZT is developing of Zak transform ( ZT) and linear canonical transform (LCT). Keywords: linear canonical Zak transform; Zak transform; linear canonical transform; linearity property; translation property. AbstrakDalam jurnal ini akan diungkapkan beberapa sifat fundamental dari transformasi Zak linear kanonik (LCZT), yaitu sifat linear dan sifat translasi. LCZT merupakan hasil pengembangan dari dua buah transformasi yaitu transformasi Zak (ZT) dan transformasi linear kanonik (LCT). Kata kunci:Transformasi Zak linear kanonik; transformasi Zak; transformasi linear kanonik; sifat linear; sifattranslasi.

Escherichia coli β-clamp slows down DNA polymerase I dependent nick translation while accelerating ligation

The nick translation property of DNA polymerase I (Pol I) ensures the maturation of Okazaki fragments by removing primer RNAs and facilitating ligation. However, prolonged nick translation traversing downstream DNA is an energy wasting futile process, as Pol I simultaneously polymerizes and depolymerizes at the nick sites utilizing energy-rich dNTPs. Using an in vitro assay system, we demonstrate that the β-clamp of the Escherichia coli replisome strongly inhibits nick translation on the DNA substrate. To do so, β-clamp inhibits the strand displacement activity of Pol I by interfering with the interaction between the finger subdomain of Pol I and the downstream primer-template junction. Conversely, β-clamp stimulates the 5’ exonuclease property of Pol I to cleave single nucleotides or shorter oligonucleotide flaps. This single nucleotide flap removal at high frequency increases the probability of ligation between the upstream and downstream DNA strands at an early phase, terminating nick translation. Besides β-clamp-mediated ligation helps DNA ligase to seal the nick promptly during the maturation of Okazaki fragments.

The fractional fourier transform and the wigner distribution

The Wigner distribution and many other members of the Cohen class of generalized phase-space distributions of a signal all share certain translation properties and the property that their two marginal distributions of energy density along the time and along the frequency axes equal the signal power and the spectral energy density. A natural generalization of this last property is shown to be a certain relationship through the Radon transform between the distribution and the signal's fractional Fourier transform. It is shown that the Wigner distribution is now distinguished by being the only member of the Cohen class that has this generalized property as well as a generalized translation property. The inversion theorem for the Wigner distribution is then extended to yield the fractional Fourier transforms.