Using TB-Sized Data to Understand Multi-Device Advertising

In this study, we combine the conversion funnel theory with machine learning methods to understand multi-device advertising. We investigate the important question of how the distribution of ads on multiple devices affects the consumer path to purchase. To handle the sheer volume of TB sized impression data, we develop a MapReduce framework to estimate the non-stationary Hidden Markov Model in parallel. To accommodate the iterative nature of the estimation procedure, we leverage the Apache Spark framework and a corporate cloud computing service. We calibrate the model with hundreds of millions of impressions for 100 advertisers. Our preliminary results show increasing the diversity of device for ads delivery can consistently encourage consumers to become more engaged. In addition, advertiser heterogeneity plays an important role in the variety of the conversion process

Deterministic learning enhanced neutral network control of unmanned helicopter

In this article, a neural network-based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design

Aqua(dimethylformamide){tris[(1-methyl-1H-benzimidazol-2-yl)methyl]amine}nickel(II) dipicrate

In the title complex, [Ni(C27H27N7)(C3H7NO)(H2O)](C6H2N3O7)2, the NiII ion is coordinated in a slightly distorted octa­hedral coordination evironment by an NiN4O2 ligand set. The tris­(N-methyl­benzimidazol-2-ylmeth­yl)amine ligand is in a tetra­dentate mode while a coordinated water molecule and a dimethyl­formamide ligand complete the coordination. In the crystal structure, inter­molecular O—H⋯O hydrogen bonds link the cation and one of the pictrate anions into four-component centrosymmetric clusters

Overview of Upgrading of Pyrolysis Oil of Biomass

AbstractPyrolysis oil, obtained from fast pyrolysis of biomass, is a promising renewable energy source which has received widespread interests for its characteristics as combustion fuels used in boiler, engines or gas turbines and resources in chemical industries. However, the pyrolysis oil as a fuel has many unfavourable properties due to its chemical composition, making it corrosive, viscose and thermally instability. Therefore, bio-oil must be properly upgraded to produce high quality biofuel for using as transportation fuels. In this review article, various types of upgrading processes have been discussed in detail including physical refining routes, chemical refining and total pyrolysis refined routes. Finally, a new upgrading route, Physical-Chemical Refining (PCR) is proposed, which will be a very promising refining route of bio-oil

Multiple-bounce Smith Microfacet BRDFs using the Invariance Principle

Smith microfacet models are widely used in computer graphics to represent materials. Traditional microfacet models do not consider the multiple bounces on microgeometries, leading to visible energy missing, especially on rough surfaces. Later, as the equivalence between the microfacets and volume has been revealed, random walk solutions have been proposed to introduce multiple bounces, but at the cost of high variance. Recently, the position-free property has been introduced into the multiple-bounce model, resulting in much less noise, but also bias or a complex derivation. In this paper, we propose a simple way to derive the multiple-bounce Smith microfacet bidirectional reflectance distribution functions (BRDFs) using the invariance principle. At the core of our model is a shadowing-masking function for a path consisting of direction collections, rather than separated bounces. Our model ensures unbiasedness and can produce less noise compared to the previous work with equal time, thanks to the simple formulation. Furthermore, we also propose a novel probability density function (PDF) for BRDF multiple importance sampling, which has a better match with the multiple-bounce BRDFs, producing less noise than previous naive approximations

Family Health Monitoring System Based on the Four Sessions Internet of Things

The accelerating pace of modern life results in the lack of effective care of people’s health status. Nowadays, resorting to the technology of the Internet of Things, we can provide home health monitoring services to minimize the impact of the disease brought to people. In this article, we proposed the realization method for the architecture of the four sections of the Internet of Things oriented to home health monitoring service, furthermore, the secondary the smoothness index method is applied to the monitoring of human health index, data from body temperature detection experiments verified the feasibility of the four sessions system, which laid firm foundations for the requirement of real-time and accuracy of the Internet of Things based home health monitoring system with a common reference significance and value in use