Structural Deep Embedding for Hyper-Networks

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.Comment: Accepted by AAAI 1

From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics

Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomenons (i.e. cascading proceese) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors get infected by a cascade after this node get infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and propose a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.Comment: 10 pages, 11 figure

Theoretical and Numerical Analysis of 1 : 1 Main Parametric Resonance of Stayed Cable Considering Cable-Beam Coupling

For the 1 : 1 main parametric resonances problems of cable-bridge coupling vibration, a main parametric resonances model considering cable-beam coupling is developed and dimensionless parametric resonances differential equations are derived. The main parametric resonances characteristics are discussed by means of multiscale approximation solution methods. Using an actual cable of cable-stayed bridge project for research object, numerical simulation analysis under a variety of conditions is illustrated. The results show that when the coupling system causes 1 : 1 parametric resonance, nonlinear main parametric resonances in response are unrelated to initial displacement of the cable, but with the increase of deck beam end vertical initial displacement increases, accompanied with a considerable “beat” vibration. When the vertical initial displacement of deck beam end is 10−6 m order of magnitude or even smaller, “beat” vibration phenomenon of cable and beam appears. Displacement amplitude of the cable is small and considerable amplitude vibration may not occur at this time, only making a slight stable “beat” vibration in the vicinity of the equilibrium position, which is different from 2 : 1 parametric resonance condition of cable-bridge coupling system. Therefore, it is necessary to limit the initial displacement excitation amplitude of beam end and prevent the occurrence of amplitude main parametric excitation resonances

Proteomic and Bioinformatics Analyses of Mouse Liver Microsomes

Microsomes are derived mostly from endoplasmic reticulum and are an ideal target to investigate compound metabolism, membrane-bound enzyme functions, lipid-protein interactions, and drug-drug interactions. To better understand the molecular mechanisms of the liver and its diseases, mouse liver microsomes were isolated and enriched with differential centrifugation and sucrose gradient centrifugation, and microsome membrane proteins were further extracted from isolated microsomal fractions by the carbonate method. The enriched microsome proteins were arrayed with two-dimensional gel electrophoresis (2DE) and carbonate-extracted microsome membrane proteins with one-dimensional gel electrophoresis (1DE). A total of 183 2DE-arrayed proteins and 99 1DE-separated proteins were identified with tandem mass spectrometry. A total of 259 nonredundant microsomal proteins were obtained and represent the proteomic profile of mouse liver microsomes, including 62 definite microsome membrane proteins. The comprehensive bioinformatics analyses revealed the functional categories of those microsome proteins and provided clues into biological functions of the liver. The systematic analyses of the proteomic profile of mouse liver microsomes not only reveal essential, valuable information about the biological function of the liver, but they also provide important reference data to analyze liver disease-related microsome proteins for biomarker discovery and mechanism clarification of liver disease

Electroacupuncture Treatment Normalized Sleep Disturbance in Morphine Withdrawal Rats

Sleep disturbance is considered as an important symptom of acute and protracted opiate withdrawal. Current results suggest that sleep disturbance may be taken as a predictor of relapse. Appropriate sleep enhancement therapy will be in favor of the retention in treatment for opiate addicts. Our previous studies have shown that electroacupuncture (EA) is effective in suppressing morphine withdrawal syndrome. The aim of the present study is to investigate the effect of 2 and 100 Hz EA on the sleep disturbance during morphine withdrawal. Rats were made dependent on morphine by repeated morphine injections (escalating doses of 5–80 mg kg−1, subcutaneously, twice a day) for 5 days. EA of 2 or 100 Hz was given twice a day for 3 days, starting at 48 h after the last morphine injection. Electroencephalogram and electromyogram were monitored at the end of the first and the last EA treatments, respectively. Results showed that non-rapid eye movement (NREM) sleep, REM sleep and total sleep time decreased dramatically, while the sleep latency prolonged significantly during acute morphine withdrawal. Both 2 and 100 Hz EA produced a significant increase in NREM sleep, REM sleep and total sleep time. It was suggested that EA could be a potential treatment for sleep disturbance during morphine withdrawal

[μ-1,4-Bis(1,2,4-triazol-1-ylmeth­yl)benzene]­bis­[aqua­(pyridine-2,6-dicarboxyl­ato)copper(II)] monohydrate

The title compound, [Cu2(C7H3NO4)2(C12H12N6)(H2O)2]·H2O, displays a discrete dinuclear structure, in which the central CuII atom is five-coordinated in a distorted square-based pyramidal coordination geometry and the flexible ligand 1,4-bis­(1,2,4-triazol-1-ylmeth­yl)benzene adopts a bis-monodentate bridging mode linking the CuII atoms. It is further assembled by O—H⋯O hydrogen-bond inter­actions involving both the coordinated and uncoordinated water molecules. The latter exhibits half-occupancy

Reverberation Time and Power Model in Indoor Wireless Scenarios

A novel, room-electromagnetics-theory-based model for reverberation time, path gain and Power Delay Profile (PDP) is proposed. Unlike the traditional models describing only the reflections, the new model takes not only reflections at boundaries, but also the effects including scattering, diffraction and air absorption along the propagation path into consideration. Extensive measurements at 2.6 GHz under Line-Of-Sight (LOS) conditions are carried out not only in enclosed structures, but also in semi-enclosed scenarios which are normally with higher average absorptive coefficients. Hence, the application of reverberation model is extended compared to open literature. Reverberation time and path gain values predicted by the proposed model are in good agreement with these measurement results obtained in various indoor wireless environments. In addition, a novel PDP model with lower complexity is proposed based on measured path gain and Nakagami-m distribution. The proposed models are proved to be more accurate than traditional reverberation models