Multi-task Deep Learning based Environment and Mobility Detection for User Behavior Modeling

International audienceCognition of user behavior can be seen as an efficient tool for automation of future mobile networks. As a matter of fact, it amplifies the intelligence of autonomic networks in a sense that the network is more aware of the operational context. However, predicting the context of mobile users is a prerequisite for inferring the user behavior. This paper deals with the user behaviour modeling. The model includes the prediction of two main features related to mobile user context: the environment and the mobility. Practically, the question is how and where the mobile user consumes the mobile services. We investigate Deep Learning based methods for simultaneously detecting the environment and the mobility state. We empirically evaluate the effectiveness of the proposed methods using real-time radio data, which has been massively gathered from multiple diversified situations of mobile users

Machine Learning with partially labeled Data for Indoor Outdoor Detection

International audienceThis paper demonstrates the feasibility of an hybrid/semi-supervised classification method for detecting the environment of an active mobile phone, based on both labeled and unlabeled cellular radio data. Precisely, we provide answers to the following question: what is the environment of the mobile user when it is/was experiencing a mobile service/application: indoor or outdoor? Implementing this method within the mobile network is interesting for mobile operators since it has low complexity, is less human intrusive (minimal intervention of mobile users) and more accurate. The semi-supervised classification algorithm learns to identify the environment using large and real collected 3GPP signals measurements. As compared to existing work, in addition to existing parameters used for classification, we propose to also use a radio metric called Timing Advance. It is computed within the mobile network. We empirically validate the innovative semi-supervised algorithm using new real-time radio measurements, with partial ground truth information, gathered daily, weekly, monthly, from indoor and outdoor locations and from multiple typical and diversified environments crossed by mobile users. The study confirms the effectiveness of the proposed scheme compared to the existing supervised classification methods including SVM and Deep Learning

Semi-supervised Deep Learning-based Methods for Indoor Outdoor Detection

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Deep Learning based Speed Profiling for Mobile Users in 5G Cellular Networks.

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Improving User Environment Detection Using Context-aware Multi-Task Deep Learning in Mobile Networks

International audienceCognition of user behavior can make future mobile networks more intelligent and flexible. Knowledge about users' habits can be used to personalize services and intelligently manage network resources. However, inferring this key information with a low-cost signaling implementation, and avoiding constant user interaction, is crucial for Mobile Network Operators (MNOs). With this motivation, this paper investigates the detection of the real-life mobile user environment using contextaware detection via multi-task learning (MTL). We propose models that are able to automatically detect up to eight distinct real-life user environments. We also improve the detection accuracy with the assistance of the mobility state profiling task. We associate both environment and mobility tasks because they correspond to the main attributes of user behavior and, additionally, both of them are correlated. Using MTL, the task of detecting environment corresponds to simultaneously answering the questions: "how and where mobile user consumes mobile services?". We build models using real-life radio data which is already available in network. This data has been massively gathered from multiple diversified situations of mobile users. Simulation results support our claim to detect several environment classes in network infrastructure with improved UED accuracy

Glycosaminoglycans and their synthetic mimetics inhibit RANTES-induced migration and invasion of human hepatoma cells.

The CC-chemokine regulated on activation, normal T-cell expressed, and presumably secreted (RANTES)/CCL5 mediates its biological activities through activation of G protein-coupled receptors, CCR1, CCR3, or CCR5, and binds to glycosaminoglycans. This study was undertaken to investigate whether this chemokine is involved in hepatoma cell migration or invasion and to modulate these effects in vitro by the use of glycosaminoglycan mimetics. We show that the human hepatoma Huh7 and Hep3B cells express RANTES/CCL5 G protein-coupled receptor CCR1 but not CCR3 nor CCR5. RANTES/CCL5 binding to these cells depends on CCR1 and glycosaminoglycans. Moreover, RANTES/CCL5 strongly stimulates the migration and the invasion of Huh7 cells and to a lesser extent that of Hep3B cells. RANTES/CCL5 also stimulates the tyrosine phosphorylation of focal adhesion kinase and activates matrix metalloproteinase-9 in Huh7 hepatoma cells, resulting in increased invasion of these cells. The fact that RANTES/CCL5-induced migration and invasion of Huh7 cells are both strongly inhibited by anti-CCR1 antibodies and heparin, as well as by beta-d-xyloside treatment of the cells, suggests that CCR1 and glycosaminoglycans are involved in these events. We then show by surface plasmon resonance that synthetic glycosaminoglycan mimetics, OTR4120 or OTR4131, directly bind to RANTES/CCL5. The preincubation of the chemokine with each of these mimetics strongly inhibited RANTES-induced migration and invasion of Huh7 cells. Therefore, targeting the RANTES-glycosaminoglycan interaction could be a new therapeutic approach for human hepatocellular carcinoma. [Mol Cancer Ther 2007;6(11):2948-58]

Alpha6beta1 integrin expressed by sperm is determinant in mouse fertilization-0

<p><b>Copyright information:</b></p><p>Taken from "Alpha6beta1 integrin expressed by sperm is determinant in mouse fertilization"</p><p>http://www.biomedcentral.com/1471-213X/7/102</p><p>BMC Developmental Biology 2007;7():102-102.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2080637.</p><p></p>integrin GoH3 (e, f) or anti-β1 integrin MB1.2 antibodies (g, h), then with Alexa Fluor488 (green) or 594 (red) goat anti-rat IgG respectively and analyzed by confocal microscopy as described in Material and methods. These molecules were distributed homogeneously with fine punctuations around the surface of the zona-intact oocytes (e, g), but both formed patches on the acid Tyrode zona-free oocyte surface (f, h). About 90 sections, representing nearly half of the eggs were collected. The maximum projection function was then used to superimpose the different sections. Transmission images corresponding to each oocyte are shown (a, b, c, and d). Bar = 20 μm