Joint Topic-Semantic-aware Social Recommendation for Online Voting

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017

A MLS-based lattice spring model for simulating elasticity of materials

A MLS-based lattice spring model is presented for numerical modeling of elasticity of materials. In the model, shear springs between particles are introduced in addition to normal springs. However, the unknowns contain only particle displacements but no particle rotations. The novelty of the model lies in that the deformations of shear springs are computed by using the local strain obtained by the moving least squares (MLS) approximation rather than using the particle displacements directly. By doing so, the proposed lattice spring model can represent the diversity of Poisson's ratio without violating the requirement of rotational invariance. Relationships between micro spring parameters and macro material constants are derived from the Cauchy-born rules and the hyperelastic theory. Numerical examples show that the proposed model is able to reproduce elastic solutions obtained by finite element methods for problems without fractures. Therefore, it is capable of simulating solid materials which are initially continuous, but eventually fracture when critical stress and/or displacement levels are reached. A demonstrating example is presented

MDM2 promotes the proliferation and inhibits the apoptosis of pituitary adenoma cells by directly interacting with p53

Introduction: Pituitary adenomas constitute one of the most common intracranial tumours. The mouse double minute 2 homologue (MDM2) is considered as an important oncogene in many tumours, but it has been little studied in pituitary adenomas and the mechanism is not well understood. The purpose of this study was to investigate the function of MDM2 and its primary mechanism of action in pituitary adenoma cells. Material and methods: The expression of MDM2 in pituitary adenoma cell lines and normal cells was determined by real-time polymerase chain reaction (RT-PCR). The proliferation and apoptosis of pituitary adenoma cells after inhibition of MDM2 expression were detected by MTS and flow cytometry, respectively. The protein expressions of MDM2 and p53 were detected by western blot. Co-IP was used to detect the direct binding between MDM2 and p53. Results: The results of RT-PCR showed that MDM2 was significantly up-regulated in pituitary adenoma cell lines. Inhibition of MDM2 suppressed the proliferation and promoted apoptosis of pituitary adenoma cells. However, inhibiting the expression of MDM2 can promotethe protein expression of p53. The results of co-IP showed that MDM2 interacted with p53 by direct combination. Then, we inhibited the expressions of p53 and MDM2 simultaneously in the pituitary adenoma cells by co-transfecting siRNAs, and the results showed that, compared with the group that inhibited MDM2 alone, cell proliferation of the co-transfected group increased and apoptosis of the cotransfected group decreased, which was similar to the NC group. Conclusions: Taken together, these results suggest that MDM2 promoted the proliferation and inhibited the apoptosis of pituitary adenoma cells by directly interacting with p53 in pituitary adenoma cells. Therefore, MDM2-p53 may serve as a novel marker and therapeutic target for pituitary adenomas

QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017

Global epidemiology of hip fractures: a study protocol using a common analytical platform among multiple countries

INTRODUCTION: Hip fractures are associated with a high burden of morbidity and mortality. Globally, there is wide variation in the incidence of hip fracture in people aged 50 years and older. Longitudinal and cross-geographical comparisons of health data can provide insights on aetiology, risk factors, and healthcare practices. However, systematic reviews of studies that use different methods and study periods do not permit direct comparison across geographical regions. Thus, the objective of this study is to investigate global secular trends in hip fracture incidence, mortality and use of postfracture pharmacological treatment across Asia, Oceania, North and South America, and Western and Northern Europe using a unified methodology applied to health records. METHODS AND ANALYSIS: This retrospective cohort study will use a common protocol and an analytical common data model approach to examine incidence of hip fracture across population-based databases in different geographical regions and healthcare settings. The study period will be from 2005 to 2018 subject to data availability in study sites. Patients aged 50 years and older and hospitalised due to hip fracture during the study period will be included. The primary outcome will be expressed as the annual incidence of hip fracture. Secondary outcomes will be the pharmacological treatment rate and mortality within 12 months following initial hip fracture by year. For the primary outcome, crude and standardised incidence of hip fracture will be reported. Linear regression will be used to test for time trends in the annual incidence. For secondary outcomes, the crude mortality and standardised mortality incidence will be reported. ETHICS AND DISSEMINATION: Each participating site will follow the relevant local ethics and regulatory frameworks for study approval. The results of the study will be submitted for peer-reviewed scientific publications and presented at scientific conferences

Dense Connected Edge Feature Enhancement Network for Building Edge Detection from High Resolution Remote Sensing Imagery

Deep-learning-based methods for building-edge-detection have been widely researched and applied in the field of image processing. However, these methods often emphasis the analysis of deep features, which may result in neglecting crucial shallow information representation. Furthermore, abstract features in the deep layers can potentially interfere with the accuracy of edge extraction. To address these challenges, we propose a densely connected edge-detection enhancement network (DCEFE-Net) for building-edge-detection in high-resolution remote sensing images. Firstly, by introducing spatial land channel attention modules, we effectively captured low-level spatial information and high-level semantic information from the input image. Secondly, the proposed edge-aware feature enhancement (EAFE) module emphasis the representation of informative edge features. By alliteratively generating multiple layers of edge-detection maps, it addresses the issue of edge detail loss and enhances edge-detection accuracy. Finally, the dense connectivity blocks strengthen the connections between the convolutional layers, thereby preventing the loss of edge features. Experimental results on the WHU and the Inria Aerial Image Labeling datasets validate the effectiveness of DCEFE-Net, as it consistently produces clear and reliable building-edge results