A Vertical Channel Model of Molecular Communication based on Alcohol Molecules

The study of Molecular Communication(MC) is more and more prevalence, and channel model of MC plays an important role in the MC System. Since different propagation environment and modulation techniques produce different channel model, most of the research about MC are in horizontal direction,but in nature the communications between nano machines are in short range and some of the information transportation are in the vertical direction, such as transpiration of plants, biological pump in ocean, and blood transportation from heart to brain. Therefore, this paper we propose a vertical channel model which nano-machines communicate with each other in the vertical direction based on pure diffusion. We first propose a vertical molecular communication model, we mainly considered the gravity as the factor, though the channel model is also affected by other main factors, such as the flow of the medium, the distance between the transmitter and the receiver, the delay or sensitivity of the transmitter and the receiver. Secondly, we set up a test-bed for this vertical channel model, in order to verify the difference between the theory result and the experiment data. At last, we use the data we get from the experiment and the non-linear least squares method to get the parameters to make our channel model more accurate.Comment: 5 pages,7 figures, Accepted for presentation at BICT 2015 Special Track on Molecular Communication and Networking (MCN). arXiv admin note: text overlap with arXiv:1311.6208 by other author

NRPA: Neural Recommendation with Personalized Attention

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation.Comment: 4 pages, 4 figure

PROACTIVE REAL-TIME WEATHER INFORMATION

A computing device (e.g., a cellular phone, a smartphone, a desktop computer, a laptop computer, a tablet computer, a portable gaming device, a watch, etc.) may provide navigation and weather information to assist a user of the computing device in selecting a route to a destination. The computing device may execute an application that provides both navigation functionality and periodic weather updates. A user of the computing device may input a destination into the application, and the application may output a route to the destination as well as weather information for locations along that route. As the user moves along the route, the application may receive updated weather information for the updated locations and alerts warning about extreme weather. The application may receive, via the computing device, the updated weather information in response to a periodic request, automatically (e.g., in response to a weather alert being issued), or a combination thereof. As a result, the application may output (e.g., visually, auditorily, tactilely, etc.) current weather information about the user’s current and upcoming locations, thus providing the user a stream of accurate weather information