Social Friend Recommendation Based on Multiple Network Correlation

© 2015 IEEE. Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, most of these websites use simple friend recommendation algorithms such as similarity, popularity, or 'friend's friends are friends,' which are intuitive but consider few of the characteristics of the social network. In this paper we investigate the structure of social networks and develop an algorithm for network correlation-based social friend recommendation (NC-based SFR). To accomplish this goal, we correlate different 'social role' networks, find their relationships and make friend recommendations. NC-based SFR is characterized by two key components: 1) related networks are aligned by selecting important features from each network, and 2) the network structure should be maximally preserved before and after network alignment. After important feature selection has been made, we recommend friends based on these features. We conduct experiments on the Flickr network, which contains more than ten thousand nodes and over 30 thousand tags covering half a million photos, to show that the proposed algorithm recommends friends more precisely than reference methods

Discriminant Projective Non-Negative Matrix Factorization

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W-T X as their coefficients, i.e., X approximate to WWT X. Since PNM

Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model

© 2017 IEEE. Precise friend recommendation is an important problem in social media. Although most social websites provide some kinds of auto friend searching functions, their accuracies are not satisfactory. In this paper, we propose a more precise auto friend recommendation method with two stages. In the first stage, by utilizing the information of the relationship between texts and users, as well as the friendship information between users, we align different social networks and choose some "possible friends." In the second stage, with the relationship between image features and users, we build a topic model to further refine the recommendation results. Because some traditional methods, such as variational inference and Gibbs sampling, have their limitations in dealing with our problem, we develop a novel method to find out the solution of the topic model based on series expansion. We conduct experiments on the Flickr dataset to show that the proposed algorithm recommends friends more precisely and faster than traditional methods

Effects of temperature and glycerol and methanol-feeding profiles on the production of recombinant galactose oxidase in Pichia pastoris

Optimization of protein production from methanol-induced Pichia pastoris cultures is necessary to ensure high productivity rates and high yields of recombinant proteins. We investigated the effects of temperature and different linear or exponential methanol-feeding rates on the production of recombinant Fusarium graminearum galactose oxidase (EC 1.1.3.9) in a P. pastoris Mut+ strain, under regulation of the AOX1 promoter. We found that low exponential methanol feeding led to 1.5-fold higher volumetric productivity compared to high exponential feeding rates. The duration of glycerol feeding did not affect the subsequent product yield, but longer glycerol feeding led to higher initial biomass concentration, which would reduce the oxygen demand and generate less heat during induction. A linear and a low exponential feeding profile led to productivities in the same range, but the latter was characterized by intense fluctuations in the titers of galactose oxidase and total protein. An exponential feeding profile that has been adapted to the apparent biomass concentration results in more stable cultures, but the concentration of recombinant protein is in the same range as when constant methanol feeding is employed. (c) 2014 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 30:728-735, 201

Effects of annealing temperature on the characteristics of Ga-doped ZnO film metal-semiconductor-metal ultraviolet photodetectors

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Hybrid of swarm intelligent algorithms in medical applications

In this paper, we designed a hybrid of swarm intelligence algorithms to diagnose hepatitis, breast tissue, and dermatology conditions in patients with such infection. The effectiveness of hybrid swarm intelligent algorithms was studied since no single algorithm is effective in solving all types of problems. In this study, feed forward and Elman recurrent neural network (ERN) with swarm intelligent algorithms is used for the classification of the mentioned diseases. The capabilities of six (6) global optimization learning algorithms were studied and their performances in training as well as testing were compared. These algorithms include: hybrid of Cuckoo Search algorithm and Levenberg-Marquardt (LM) (CSLM), Cuckoo Search algorithm (CS) and backpropagation (BP) (CSBP), CS and ERN (CSERN), Artificial Bee Colony (ABC) and LM (ABCLM), ABC and BP (ABCBP), Genetic Algorithm (GA) and BP (GANN). Simulation comparative results indicated that the classification accuracy and run time of the CSLM outperform the CSERN, GANN, ABCBP, ABCLM, and CSBP in the breast tissue dataset. On the other hand, the CSERN performs better than the CSLM, GANN, ABCBP, ABCLM, and CSBP in both th

Myotoxicity of telbivudine in pre-existing muscle damage

<p>Abstract</p> <p>Objectives</p> <p>It is unknown if telbivudine causes muscle damage only in patients with pre-existing muscle pathology.</p> <p>Case report</p> <p>A 27 yo male of African origin received telbivudine for hepatitis B during 3 months. Three weeks after initiation of the drug he developed myalgia, and tiredness. Creatine-kinase increased from 278 U/l (n, <170 U/l) at baseline to 3243 U/l. Shortly after discontinuation of telbivudine muscle symptoms and hyper-CK-emia disappeared. The findings suggest that pre-existing muscle damage favored the myotoxic effect of telbivudine.</p> <p>Conclusions</p> <p>Telbivudine appears to cause accelerated muscle toxicity if given to patients who already have muscle damage. Patients under telbivudine should be closely monitored for muscular side effects and those with pre-existing muscle damage should not receive the drug.</p

Accurate and linear time pose estimation from points and lines

The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms, the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.Peer ReviewedPostprint (author's final draft

Thermal Properties of Isotopically Engineered Graphene

In addition to its exotic electronic properties graphene exhibits unusually high intrinsic thermal conductivity. The physics of phonons - the main heat carriers in graphene - was shown to be substantially different in two-dimensional (2D) crystals, such as graphene, than in three-dimensional (3D) graphite. Here, we report our experimental study of the isotope effects on the thermal properties of graphene. Isotopically modified graphene containing various percentages of 13C were synthesized by chemical vapor deposition (CVD). The regions of different isotopic composition were parts of the same graphene sheet to ensure uniformity in material parameters. The thermal conductivity, K, of isotopically pure 12C (0.01% 13C) graphene determined by the optothermal Raman technique, was higher than 4000 W/mK at the measured temperature Tm~320 K, and more than a factor of two higher than the value of K in a graphene sheets composed of a 50%-50% mixture of 12C and 13C. The experimental data agree well with our molecular dynamics (MD) simulations, corrected for the long-wavelength phonon contributions via the Klemens model. The experimental results are expected to stimulate further studies aimed at better understanding of thermal phenomena in 2D crystals.Comment: 14 pages, 3 figure

Image Signal Processor parameter tuning with surrogate-assisted Particle Swarm Optimization

International audienceEvolutionary algorithms (EA) are developed and compared based on well defined benchmark problems, but their application to real-world problems is still challenging. In image processing, EA have been used to tune a particular image filter or in the design of filters themselves. But nowadays in digital cameras, the image sensor captures a raw image that is then processed by an Image Signal Processor (ISP) where several transformations or filters are sequentially applied in order to enhance the final picture. Each of these steps have several parameters and their tuning require lot of resources that are usually performed by human experts based on metrics to assess the quality of the final image. This can be considered as an expensive black-box optimization problem with many parameters and many quality metrics. In this paper, we investigate the use of EA in the context of ISP parameter tuning with the aim of raw image enhancement