Estrada index of hypergraphs via eigenvalues of tensors

A uniform hypergraph $\mathcal{H}$ is corresponding to an adjacency tensor $\mathcal{A}_\mathcal{H}$. We define an Estrada index of $\mathcal{H}$ by using all the eigenvalues $\lambda_1,\dots,\lambda_k$ of $\mathcal{A}_\mathcal{H}$ as $\sum_{i=1}^k e^{\lambda_i}$. The bounds for the Estrada indices of uniform hypergraphs are given. And we characterize the Estrada indices of $m$-uniform hypergraphs whose spectra of the adjacency tensors are $m$-symmetric. Specially, we characterize the Estrada indices of uniform hyperstars

METS-Based Cataloging Toolkit for Digital Library Management System

This toolkit is designed for the Digital Library Management System of Tsinghua University (TH-DLMS). The aim of TH-DLMS is to build up a platform to preserve various kinds of digitalized resources, manage distributed repositories and provide kinds of service for research and education. This toolkit fulfills the cataloging and preservation functions of TH-DLMS. METS (Metadata Encoding and T ransmission Standard) encoded documents are used as the final storage format of metadata, including descriptive metadata, structural metadata and administrative metadata, and submitted to a management system based on Fedora (Flexible Extensible Digital Object and Repository Architecture)

Seagrass distribution changes in Swan Lake of Shandong Peninsula from 1979 to 2009 inferred from satellite remote sensing data

Seagrass and associated bio-resources are very important for swan’s overwintering in Swan Lake in Rongcheng of Shandong Peninsula of China. The seagrass distribution changes, which are usually affected by the regional human activities, can indirectly affect swan’s habitat. In this study the satellite remote sensing data in years 1979–2009 together with in-situ observations in recent years were used to examine the seagrass distribution changes in Swan Lake. The band ratio of band 1 to band 2, Lyzenga’s methods and band synthesize of band 1, band 2 and band 3 were used for seagrass retrieval. The band ratio of band 1 to band 2 with ranges greater than 4.5 was used for estimating the seagrass coverage greater than 50%. Results showed that in years 1979–1990 seagrass coverage greater than 50% occupied more than half of the surface area of Swan Lake. In years 2000–2005, the total area with seagrass distributions reduced greatly, only about one sixth to one fourth of Swan Lake’s surface area. After 2005, the seagrass area in Swan Lake increased gradually and occasionally was greater than one third of the total surface area of the Lake. It was shown that human activities such as the dam and fish pond establishment and the awareness of seagrass importance and protected actively result in the seagrass distributions changes in Swan Lake which decreased first and then increased afterwards

A gauss function based approach for unbalanced ontology matching

Ontology matching, aiming to obtain semantic correspon-dences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the appli-cations cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community. In this paper, we propose a novel Gauss Function based ontology matching approach to deal with this unbalanced ontology matching issue. Given a relative lightweight on-tology which represents the local domain knowledge, we ex-tract a“similar ” sub-ontology from the corresponding heavy-weight ontology and then carry out the matching procedure between this lightweight ontology and the newly generated sub-ontology. The sub-ontology generation is based on the influences between concepts in the heavyweight ontology. We propose a Gauss Function based method to properly cal-culate the influence values between concepts. In addition, we perform an extensive experiment to verify the effective-ness and efficiency of our proposed approach by using OAEI 2007 tasks. Experimental results clearly demonstrate that our solution outperforms the existing methods in terms of precision, recall and elapsed time

FCP-Net: A Feature-Compression-Pyramid Network Guided by Game-Theoretic Interactions for Medical Image Segmentation

Medical image segmentation is a crucial step in diagnosis and analysis of diseases for clinical applications. Deep neural network methods such as DeepLabv3+ have successfully been applied for medical image segmentation, but multi-level features are seldom integrated seamlessly into different attention mechanisms, and few studies have explored the interactions between medical image segmentation and classification tasks. Herein, we propose a feature-compression-pyramid network (FCP-Net) guided by game-theoretic interactions with a hybrid loss function (HLF) for the medical image segmentation. The proposed approach consists of segmentation branch, classification branch and interaction branch. In the encoding stage, a new strategy is developed for the segmentation branch by applying three modules, e.g., embedded feature ensemble, dilated spatial mapping and channel attention (DSMCA), and branch layer fusion. These modules allow effective extraction of spatial information, efficient identification of spatial correlation among various features, and fully integration of multireceptive field features from different branches. In the decoding stage, a DSMCA module and a multi-scale feature fusion module are used to establish multiple skip connections for enhancing fusion features. Classification and interaction branches are introduced to explore the potential benefits of the classification information task to the segmentation task. We further explore the interactions of segmentation and classification branches from a game theoretic view, and design an HLF. Based on this HLF, the segmentation, classification and interaction branches can collaboratively learn and teach each other throughout the training process, thus applying the conjoint information between the segmentation and classification tasks and improving the generalization performance. The proposed model has been evaluated using several datasets, including ISIC2017, ISIC2018, REFUGE, Kvasir-SEG, BUSI, and PH2, and the results prove its competitiveness compared with other state-of-the-art techniques