Discrete Factorization Machines for Fast Feature-based Recommendation

User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss. This work is accepted by IJCAI 2018.Comment: Appeared in IJCAI 201

DFM synthesis approach based on product-process interface modelling. Application to the peen forming process.

Engineering design approach are curently CAD-centred design process. Manufacturing information is selected and assessed very late in the design process and above all as a reactive task instead of being proactive to lead the design choices. DFM appraoches are therefore assesment methods that compare several design alternatives and not real design approaches at all. Main added value of this research work concerns the use of a product-process interface model to jointly manage both the product and the manufacturing data in a proactive DFM way. The DFM synthesis approach and the interface model are presented via the description of the DFM software platform

A Stepwise Efficiency Improvement DEA Model for Airport Operations with Fixed Production Factors

In the spirit of the deregulation movement, Japan is also faced with an ÃgAsia Open SkyÃh agreement which favours aviation liberalization in international services. This means an end to Japan's aviation policy of isolation. In association with this policy change, also environmental concerns grew increasingly severe for small and local regional airports. Consequently, there is a need for an objective analysis of the efficiency of airport operations in Japan. A standard tool to judge the efficiency of such activities is Data Envelopment Analysis (DEA). In the past years, much progress has been made to extend this approach in various directions. Interesting examples are the Distance Friction Minimization (DFM) model and the Context-Dependent (CD) model. The DFM model is based on a generalized distance friction function and serves to improve the performance of a Decision Making Unit (DMU) by identifying the most appropriate movement towards the efficiency frontier surface. Standard DEA models use a uniform input reduction in the improvement projections, but the DFM approach aims to enhance efficiency strategies by introducing a weighted projection function. This approach may address both input reduction and output increase as a strategy of a DMU. Likewise, the CD model yields efficient frontiers at different levels, while it is based on a level-by-level improvement projection. The Stepwise DFM model is an integration of the DFM and the CD model in order to design a stepwise efficiency-improving projection model for a conventional DEA. In general, a DEA model – and neither the mix of the DFM-CD model – doesnÃft take into account a fixed factor. Such a non-controllable of fixed factor may refer to a production factor that cannot be flexibly adjusted in the short run. In our study the newly integrated Stepwise DFM-CD model will be extended with a fixed factor model in order to adapt the DEA model to realistic circumstances in an efficiency improvement projection. The above-mentioned stepwise fixed factor projection model is illustrated on the basis of an application to the efficiency analysis of airport operations in Japan in light of the above mentioned contextual changes in aviation policy.

Forecasting Housing Prices: Dynamic Factor Model versus LBVAR Model

The purpose of this paper is to compare the forecasting power of DFM and LBVAR models as they are used to forecast house price growth rates for 42 metropolitan areas in the United States. The forecasting performances of these two large-scale models are compared based on the Theil U-statistic.Housing market, DFM, LBVAR, dynamic PCA, Demand and Price Analysis,

Effect of Direct-Fed Microbials and Enzyme Supplementation in Prepartum Holstein Cows on Colostrum and Calf Immunity

In cows, colostrum is composed of several antibodies and nutrients to provide immunity and energy to the calf. Feeding calves high quality colostrum has been shown to improve calf health, leading to reduced mortality in calves and greater milk production in cows. The addition of direct-fed microbials (DFM) to cow diets has been theorized to improve feed efficiency and milk production, with studies showing mixed results. However, few experiments have studied the effect of feeding DFM on colostrum quality. In this experiment two treatments were given, 1) DFM and 2) DFM and enzymes (DFME). Colostrum was analyzed to determine if yield, composition, and immunoglobulin (IgG and IgA) concentration were affected. Calf serum IgG and IgA concentrations were analyzed to determine if 24 h concentrations and apparent efficiency of absorption (AEA) were affected. There were no differences with regard to yield or IgA concentration. The percent of ash showed a positive trend, indicating a higher percentage with the treatments (P = 0.067). The treatments had no effect on the additional components analyzed. The results for the IgG concentration were not significant although an increase was observed from 79.1 mg/mL in the control to 91.1 mg/mL in the DFME treatment. Neither treatment had an effect on calf immunoglobulin concentration or AEA. Based on the results, feeding DFM or DFME improves percent ash and might increase IgG concentration, but further research is necessary

Characterization of the asymptotic distribution of semiparametric M-estimators

This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification. Our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature. The first-stage nonparametric estimation may depend on finite dimensional parameters. We characterize: (1) conditions under which the first-stage estimation of nonparametric components do not affect the asymptotic distribution, (2) conditions under which the asymptotic distribution is affected by the derivatives of the first-stage nonparametric estimator with respect to the finite-dimensional parameters, and (3) conditions under which one can allow non-smooth objective functions. Our framework is illustrated by applying it to three examples: (1) profiled estimation of a single index quantile regression model, (2) semiparametric least squares estimation under model misspecification, and (3) a smoothed matching estimator. © 2010 Elsevier B.V. All rights reserved

Stable Frank-Kasper phases of self-assembled, soft matter spheres

Single molecular species can self-assemble into Frank Kasper (FK) phases, finite approximants of dodecagonal quasicrystals, defying intuitive notions that thermodynamic ground states are maximally symmetric. FK phases are speculated to emerge as the minimal-distortional packings of space-filling spherical domains, but a precise quantitation of this distortion and how it affects assembly thermodynamics remains ambiguous. We use two complementary approaches to demonstrate that the principles driving FK lattice formation in diblock copolymers emerge directly from the strong-stretching theory of spherical domains, in which minimal inter-block area competes with minimal stretching of space-filling chains. The relative stability of FK lattices is studied first using a diblock foam model with unconstrained particle volumes and shapes, which correctly predicts not only the equilibrium {\sigma} lattice, but also the unequal volumes of the equilibrium domains. We then provide a molecular interpretation for these results via self-consistent field theory, illuminating how molecular stiffness regulates the coupling between intra-domain chain configurations and the asymmetry of local packing. These findings shed new light on the role of volume exchange on the formation of distinct FK phases in copolymers, and suggest a paradigm for formation of FK phases in soft matter systems in which unequal domain volumes are selected by the thermodynamic competition between distinct measures of shape asymmetry.Comment: 40 pages, 22 figure