Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net

Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time. Therefore, top-performing deep models of high depth and complexity are not well suited for these applications with the limitations on the inference time. In order to further improve the neural networks' performance given the time and computational limitations, we propose an approach that exploits a cumbersome net to help train the lightweight net for prediction. We dub the whole process rocket launching, where the cumbersome booster net is used to guide the learning of the target light net throughout the whole training process. We analyze different loss functions aiming at pushing the light net to behave similarly to the booster net, and adopt the loss with best performance in our experiments. We use one technique called gradient block to improve the performance of the light net and booster net further. Experiments on benchmark datasets and real-life industrial advertisement data present that our light model can get performance only previously achievable with more complex models.Comment: 10 pages, AAAI201

Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate~(CTR) prediction, whose goal is to estimate the probability of the user clicks, has become one of the core tasks in advertising systems. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external environment and the internal cognition, user interest evolves over time dynamically. There are several CTR prediction methods for interest modeling, while most of them regard the representation of behavior as the interest directly, and lack specially modeling for latent interest behind the concrete behavior. Moreover, few work consider the changing trend of interest. In this paper, we propose a novel model, named Deep Interest Evolution Network~(DIEN), for CTR prediction. Specifically, we design interest extractor layer to capture temporal interests from history behavior sequence. At this layer, we introduce an auxiliary loss to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, we propose interest evolving layer to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution. In the experiments on both public and industrial datasets, DIEN significantly outperforms the state-of-the-art solutions. Notably, DIEN has been deployed in the display advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201

[(4S,5S)-2,2-Dimethyl-1,3-dioxolane-4,5-di­yl]bis­[N-(thio­phen-2-yl­methyl­idene)methanamine]

In the title compound, C17H20N2O2S2, the five-membered heterocycle exhibits an envelope conformation and the mol­ecular chirality and configuration are well preserved from l-tartaric acid. The dihedral angle between the two thio­phene rings is 17.0 (2)°. In the crystal, mol­ecules are linked by C—H⋯O and C—H⋯S hydrogen inter­actions, which are effective in the stabilization of the crystal structure

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

Liquid slug motion in an oscillatory capillary tube.

Single liquid slug motion in a horizontally oscillating circular tube is investigated---the liquid does not completely wet the tube wall and the slug velocity is not sufficiently large to deposit a macro-scale, Newtonian film. The focus of this study is to model the oscillatory contact line that constitutes the boundary condition for flows with interface---the moving contact line problem is singular in a traditional fluid dynamics sense. Additionally, mean motion of a slug through asymmetric forcing is sought. Corresponding experiments are conducted for a borosilicate glass tube and a treated water slug. For small tube motions where the contact lines are pinned or governed by a slip coefficient assumed small, spectral eigenvalue methods along with some lower-dimensional approximations are used to determine the natural frequencies of a liquid slug with curved end caps. The numerical results agree well with a spherical end cap approximation (0-D) for large aspect ratio slugs and with a membrane approximation (1-D) for small aspect ratios. The experimental observations for different aspect ratios agree well with the predictions, although the gravity, viscosity and/or slip are neglected in the analyses. Most previous oscillatory contact line models were based on observations of unidirectional creeping flows (Young & Davis-, Hocking). We pose a universal dynamic contact line model that accounts for unsteady effect based directly on experiments. This universal contact line model agrees well with the observations for a large range of frequencies for sinusoidal oscillations and does not have the restrictions as used by Miles (1990). The model is incorporated into a zero-dimensional simulation where viscosity and surface tension are treated in an approximate way. The simulation for the sinusoidal tube motions agrees well with the measurements. A net motion of the slug is achieved when the tube motion is asymmetric directionally, i.e., a periodic t2 function. The optimal pumping effect is studied experimentally as well as with a zero dimensional approach. The simulation predicts the region where the maximum pumping occurs, but the maximum pumping efficiency predicted is larger than the experiments. The mechanism has potential application for fluid handling in surface tension dominant flows.Ph.D.Applied SciencesMechanical engineeringOcean engineeringPlasma physicsPure SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124019/2/3121893.pd

A hybrid PSO-SVM-based model for determination of oil recovery factor in the low-permeability reservoir

Oil recovery factor is one of the most important parameters in the development process of oil reservoir, especially in the low-permeability reservoir. In general, the determination of recovery factor can be obtained either experimentally or numerically. Experimental method is often time-consuming and expensive, while numerical method has been always confined to narrow range of application or relatively large error. Recently, an intelligent method has been proven as an efficient tool to model the complex and nonlinear phenomena. In this work, an intelligent model based on support vector machine in combination with the particle swarm optimization (PSO-SVM) technique was established to predict oil recovery factor in the low-permeability reservoir. Input variables of the proposed PSO-SVM model with the aid of a grey correlation analysis method are permeability, well spacing density, production-injection well ratio, porosity, effective thickness, crude oil viscosity and output parameter is oil recovery factor of low-permeability reservoir. The accuracy and reliability of the proposed model were evaluated through 34 data sets collected in the open literature and compared with PSO-BP neural network, empirical method from Oil and Gas Company. The results indicated that the PSO-SVM model gives the best results with average absolute relative deviation (AARD) of 3.79%, while AARDs for the PSO-BP neural network and empirical method are 9.18% and 10.0%, respectively. Furthermore, outlier detection was used on the basis of whole data sets to definite the valid domains of PSO-SVM and PSO-BP models by detecting the probable doubtful recovery factor data in the low-permeability reservoir

A Review of CO

In 2008, the top CO2 emitters were China, United States, and European Union. The rapid growing economy and the heavy reliance on coal in China give rise to the continued growth of CO2 emission, deterioration of anthropogenic climate change, and urgent need of new technologies. Carbon Capture and sequestration is one of the effective ways to provide reduction of CO2 emission and mitigation of pollution. Coal-fired power plants are the focus of CO2 source supply due to their excessive emission and the energy structure in China. And over 80% of the large CO2 sources are located nearby storage reservoirs. In China, the CO2 storage potential capacity is of about 3.6 × 109 t for all onshore oilfields; 30.483 × 109 t for major gas fields between 900 m and 3500 m of depth; 143.505 × 109 t for saline aquifers; and 142.67 × 109 t for coal beds. On the other hand, planation, soil carbon sequestration, and CH4–CO2 reforming also contribute a lot to carbon sequestration. This paper illustrates some main situations about CO2 sequestration applications in China with the demonstration of several projects regarding different ways of storage. It is concluded that China possesses immense potential and promising future of CO2 sequestration