Community Core Evolution in Mobile Social Networks

Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks

Propagation Characteristics Of Density Currents And Implications To Pollutant Transport In A Stratified Reservoir

With global warming, the frequency and intensity of extreme rainfall events were predicted to change more dramatically in the near future while the amount of total precipitation will change slightly. Large volume of turbid inflow will enter the source water reservoir after a heavy rainfall, and evolve in various types of density currents depending on the density difference between the inflow and background water. Density currents play an important role in the thermal structure and pollutant transport in the reservoir. Understanding the behaviors of density current is fundamental to study the changes of source water quality during the flooding season. Characteristics of density currents were first experimentally investigated in a pilot stratified reservoir with a length of 2.0m and a depth of 0.54m, in which the thermal stratification was achieved with a heating method. When the stratification stability indexes were of 0.0112～0.0197 m-1 and the buoyancy frequencies were of 0.3314～0.4393 s-1, the turbid inflow was observed to separate from the bed slope and to propagate horizontally into its equilibrium layer, namely interflow. The separation depth of density currents and the thickness of the interflow were both smaller in the strong stratification cases than those in the weak cases, which had an important impact on the pollutant transport in the reservoir. Propagation characteristics of density currents and its implications to pollutant transport were systemically explored by numerically simulating behaviors of density currents under different conditions of stratification stability index, inflow velocity and sediment content of inflow. After careful calibration of Euler-Euler model, the simulated separation depth of density currents and the thickness of the interflow agreed well with the experimental ones, which showed the propagation of inflow was closely related to the stratification level. Impacts of inflow velocity and sediment content of inflow on the propagation of density currents were different under the simulated conditions. When the volume fraction of sediment in the inflow was increased from 0.025% to 0.20%, the separation depth of density currents was decreased from 21.0cm to 18.5cm, the thickness of the interflow was slightly increased from 6.2cm to 7.8cm, but the heights of the internal hydraulic jump were almost the same. The inflow velocity mainly influenced the time of developing the interflow, the developing time decreased as the inflow velocity increased, which implied the water quality would deteriorate quickly after a heavy rainfall. Under larger inflow velocity conditions, mixing between the inflow and background water was stronger due to the higher energy carried by the inflow, and this caused the larger depth of interflow and the bigger height of internal hydraulic jump, which indicated the pollutants carried by turbid inflow would be transported more widely

Age as a risk factor for acute mountain sickness upon rapid ascent to 3,700 m among young adult Chinese men.

BackgroundThe aim of this study was to explore the relationship between age and acute mountain sickness (AMS) when subjects are exposed suddenly to high altitude.MethodsA total of 856 young adult men were recruited. Before and after acute altitude exposure, the Athens Insomnia Scale score (AISS) was used to evaluate the subjective sleep quality of subjects. AMS was assessed using the Lake Louise scoring system. Heart rate (HR) and arterial oxygen saturation (SaO2) were measured.ResultsResults showed that, at 500 m, AISS and insomnia prevalence were higher in older individuals. After acute exposure to altitude, the HR, AISS, and insomnia prevalence increased sharply, and the increase in older individuals was more marked. The opposite trend was observed for SaO2. At 3,700 m, the prevalence of AMS increased with age, as did severe AMS, and AMS symptoms (except gastrointestinal symptoms). Multivariate logistic regression analysis showed that age was a risk factor for AMS (adjusted odds ratio [OR] 1.07, 95% confidence interval [CI] 1.01-1.13, P<0.05), as well as AISS (adjusted OR 1.39, 95% CI 1.28-1.51, P<0.001).ConclusionThe present study is the first to demonstrate that older age is an independent risk factor for AMS upon rapid ascent to high altitude among young adult Chinese men, and pre-existing poor subjective sleep quality may be a contributor to increased AMS prevalence in older subjects

Potential of DNMT and its Epigenetic Regulation for Lung Cancer Therapy

Lung cancer, the leading cause of mortality in both men and women in the United States, is largely diagnosed at its advanced stages that there are no effective therapeutic alternatives. Although tobacco smoking is the well established cause of lung cancer, the underlying mechanism for lung tumorigenesis remains poorly understood. An important event in tumor development appears to be the epigenetic alterations, especially the change of DNA methylation patterns, which induce the most tumor suppressor gene silence. In one scenario, DNA methyltransferase (DNMT) that is responsible for DNA methylation accounts for the major epigenetic maintenance and alternation. In another scenario, DNMT itself is regulated by the environment carcinogens (smoke), epigenetic and genetic information. DNMT not only plays a pivotal role in lung tumorigenesis, but also is a promising molecular bio-marker for early lung cancer diagnosis and therapy. Therefore the elucidation of the DNMT and its related epigenetic regulation in lung cancer is of great importance, which may expedite the overcome of lung cancer

Optimal Batched Best Arm Identification

We study the batched best arm identification (BBAI) problem, where the learner's goal is to identify the best arm while switching the policy as less as possible. In particular, we aim to find the best arm with probability $1-\delta$ for some small constant $\delta>0$ while minimizing both the sample complexity (total number of arm pulls) and the batch complexity (total number of batches). We propose the three-batch best arm identification (Tri-BBAI) algorithm, which is the first batched algorithm that achieves the optimal sample complexity in the asymptotic setting (i.e., $\delta\rightarrow 0$) and runs only in at most $3$ batches. Based on Tri-BBAI, we further propose the almost optimal batched best arm identification (Opt-BBAI) algorithm, which is the first algorithm that achieves the near-optimal sample and batch complexity in the non-asymptotic setting (i.e., $\delta>0$ is arbitrarily fixed), while enjoying the same batch and sample complexity as Tri-BBAI when $\delta$ tends to zero. Moreover, in the non-asymptotic setting, the complexity of previous batch algorithms is usually conditioned on the event that the best arm is returned (with a probability of at least $1-\delta$), which is potentially unbounded in cases where a sub-optimal arm is returned. In contrast, the complexity of Opt-BBAI does not rely on such an event. This is achieved through a novel procedure that we design for checking whether the best arm is eliminated, which is of independent interest.Comment: 32 pages, 1 figure, 3 table