The Directed Dominating Set Problem: Generalized Leaf Removal and Belief Propagation

A minimum dominating set for a digraph (directed graph) is a smallest set of vertices such that each vertex either belongs to this set or has at least one parent vertex in this set. We solve this hard combinatorial optimization problem approximately by a local algorithm of generalized leaf removal and by a message-passing algorithm of belief propagation. These algorithms can construct near-optimal dominating sets or even exact minimum dominating sets for random digraphs and also for real-world digraph instances. We further develop a core percolation theory and a replica-symmetric spin glass theory for this problem. Our algorithmic and theoretical results may facilitate applications of dominating sets to various network problems involving directed interactions.Comment: 11 pages, 3 figures in EPS forma

Composition of gut microbiota in infants in China and global comparison

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Machine learning approach applied to the prevalence analysis of ADHD symptoms in young adults of Barranquilla, Colombia

Disorder Attention Deficit/Hyperactivity Disorder, or ADHD, is recognized as one of the pathologies of high prevalence in children and adolescents from the global environment population; this disorder generates visible symptoms usually diminish with the passage of time in adulthood, however they remain concealed by demonstrations damnifican personal stability and human development apt. This article shows the results of the research aimed at determining the prevalence of symptoms of attention deficit hyperactivity disorder in Young Adults University of Barranquilla and its Metropolitan Area. The sample of 1600 young adults between 18 and 25 years, which has been estimated at 95% confidence level and a margin of error of 2.44%. The information was acquired through the application of exploratory instruments symptoms of attention deficit hyperactivity disorder. With the application of the algorithm different machine learning algorithms such as: Bagging, MultiBoostAB, DecisionStump, LogitBoost, FT, J48Graft, a high performance in the Bagging algorithm could be identified with the following results in quality metrics: Accuracy 91.67%, Precision 94.12%, Recall 88.89% and F-measure 91.43%

Higgsing M2 to D2 with gravity: N=6 chiral supergravity from topologically gauged ABJM theory

We present the higgsing of three-dimensional N=6 superconformal ABJM type theories coupled to conformal supergravity, so called topologically gauged ABJM theory, thus providing a gravitational extension of previous work on the relation between N M2 and N D2-branes. The resulting N=6 supergravity theory appears at a chiral point similar to that of three-dimensional chiral gravity introduced recently by Li, Song and Strominger, but with the opposite sign for the Ricci scalar term in the lagrangian. We identify the supersymmetry in the broken phase as a particular linear combination of the supersymmetry and special conformal supersymmetry in the original topologically gauged ABJM theory. We also discuss the higgsing procedure in detail paying special attention to the role played by the U(1) factors in the original ABJM model and the U(1) introduced in the topological gauging.Comment: 53 pages, Late

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines

Dynamic distribution and expression in vivo of the human interferon gamma gene delivered by adenoviral vector

<p>Abstract</p> <p>Background</p> <p>We previously found that r-hu-IFNγ exerts a potent anti-tumor effect on human nasopharyngeal carcinoma xenografts <it>in vivo</it>. Considering the fact that the clinical use of recombinant IFNγ is limited by its short half-life and systemic side effects, we developed a recombinant adenovirus, Ad-IFNγ.</p> <p>Methods</p> <p>Dynamic distribution of the adenovirus vector and expression of IFNγ were evaluated by Q-PCR and ELISA after intratumoral administration of Ad-IFNγ into CNE-2 xenografts.</p> <p>Results</p> <p>Ad-IFNγ DNA was mainly enriched in tumors where the Ad-IFNγ DNA was injected (<it>P </it>< 0.05, compared to blood or parenchymal organs), as well as in livers (<it>P </it>< 0.05). Concentrations of Ad-IFNγ DNA in other organs and blood were very low. Intratumoral Ad-IFNγ DNA decreased sharply at high concentrations (9 × 10⁵copies/μg tissue DNA), and slowly at lower concentrations (1.7–2.9 × 10⁵copies/μg tissue DNA). IFNγ was detected in the tumors and parenchymal organs. The concentration of IFNγ was highest in the tumor (<it>P </it>< 0.05), followed by the liver and kidney (<it>P </it>< 0.05). High-level intratumoral expression of IFNγ was maintained for at least 7 days, rapidly peaking on day 3 after injection of Ad-IFNγ DNA.</p> <p>Conclusion</p> <p>An IFNγ gene delivered by an adenoviral vector achieved high and consistent intratumoral expression. Disseminated Ad-IFNγ DNA and the transgene product were mainly enriched in the liver.</p