Exploiting symmetries for scaling loopy belief propagation and relational training

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. A prominent example are relational models that capture complexity. Exploiting these symmetries, however, has not been considered for scaling yet. In this paper, we show that inference and training can indeed benefit from exploiting symmetries. Specifically, we show that (loopy) belief propagation (BP) can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified BP running on the lifted graph yields the same marginals as BP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able. Still, in many if not most situations training relational models will not benefit from this (scalable) lifting: symmetries within models easily break since variables become correlated by virtue of depending asymmetrically on evidence. An appealing idea for such situations is to train and recombine local models. This breaks long-range dependencies and allows to exploit lifting within and across the local training tasks. Moreover, it naturally paves the way for the first scalable lifted training approaches based on stochastic gradients, both in an online and a MapReduced fashion. On several datasets, the online training, for instance, converges to the same quality solution over an order of magnitude faster, simply because it starts optimizing long before having seen the entire mega-example even once.     

Genetic-based random key generator (GRKG): a new method for generating more-random keys for one-time pad cryptosystem

In this paper, a new algorithm for generating more-randomized keys for symmetrical cipher one-time pad (OTP) according to the linear congruential (LCG) method based on the idea of genetic algorithm is proposed. The method, genetic-based random key generator, is proposed for generating keys for the OTP method with a high degree of key randomness; this adds more strength to the OTP method against breaking this cryptosystem. This algorithm is composed of two parts. Initially, the first population is being generated by LCG method, and then, genetic operators for generating the next populations are being used. Generating random keys with the presented method requires seven-parameter key that increases the security of communication between the transceivers.     

Ultrasonic Attenuation Due to Grain Boundary Scattering in Pure Niobium

Experimental confirmation of various grain scattering theories exist but there are still outstanding questions concerning the characterization of microstructure using ultrasound. In this study, high purity niobium serves as a model material devoid of extrinsic scattering centers (e.g., voids, precipitates, second phase particles etc.) A range of microstructures were obtained by annealing in ultrahigh vacuum at different temperatures (600–800°C) after routine cold-rolling and recrystallization heat treatment. Ultrasonic attenuation is measured as a function of a frequency for each sample. For the samples with an intermediate grain size (typically ∼50 μm), attenuation follows a power law dependence on frequency with an exponent of n∼1.6, which is close to the prediction, n=2, of classical Stochastic scattering theory. However, quantitative comparison shows that the observed attenuation is higher than predicted by the classical theory. The Stanke-Kino unified grain scattering theory may provide an explanation for the lower frequency dependence than traditional theories predict, though it still under predicts the magnitude of the attenuation. In any event, the resulting empirical relations provide a useful approach for practical grain size measurement with an acceptable level of uncertainty. The effect of a layered microstructure typical of some sheets/plates is discussed.     

A transformation technique to estimate the process capability index for non-normal processes

Estimating the process capability index (PCI) for non-normal processes has been discussed by many researches. There are two basic approaches to estimating the PCI for non-normal processes. The first commonly used approach is to transform the non-normal data into normal data using transformation techniques and then use a conventional normal method to estimate the PCI for transformed data. This is a straightforward approach and is easy to deploy. The alternate approach is to use non-normal percentiles to calculate the PCI. The latter approach is not easy to implement and a deviation in estimating the distribution of the process may affect the efficacy of the estimated PCI. The aim of this paper is to estimate the PCI for non-normal processes using a transformation technique called root transformation. The efficacy of the proposed technique is assessed by conducting a simulation study using gamma, Weibull, and beta distributions. The root transformation technique is used to estimate the PCI for each set of simulated data. These results are then compared with the PCI obtained using exact percentiles and the Box-Cox method. Finally, a case study based on real-world data is presented.     

A two-phase linear programming methodology for fuzzy multi-objective mixed-model assembly line problem

We develop a fuzzy multi-objective linear programming (FMOLP) model for solving multi-objective mixed-model assembly line problem. In practice, vagueness and imprecision of the goals in this problem make the fuzzy decision-making complicated. The proposed model considers minimizing total utility work, total production rate variation, and total setup cost, using a two-phase linear programming approach. In the first phase, the problem is solved using a max–min approach. The max–min solution not being efficient, in general, we propose a new model in the second phase to maximize a composite satisfaction degree at least as good as the degrees obtained by phase one. To show the effectiveness of the proposed approach, a numerical example is solved and the results are compared with the ones obtained by the fuzzy mixed integer goal programming and weighted additive methods. The computational results show that the proposed FMOLP model achieves lower objective functions as well as higher satisfaction degrees.     

Modal Identification Study of Vincent Thomas Bridge Using Simulated Wind-Induced Ambient Vibration Data

Abstract:   In this article, wind-induced vibration response of Vincent Thomas Bridge, a suspension bridge located in San Pedro near Los Angeles, California, is simulated using a detailed three-dimensional finite element model of the bridge and a state-of-the-art stochastic wind excitation model. Based on the simulated wind-induced vibration data, the modal parameters (natural frequencies, damping ratios, and mode shapes) of the bridge are identified using the data-driven stochastic subspace identification method. The identified modal parameters are verified by the computed eigenproperties of the bridge model. Finally, effects of measurement noise on the system identification results are studied by adding zero-mean Gaussian white noise processes to the simulated response data. Statistical properties of the identified modal parameters are investigated under an increasing level of measurement noise. The framework presented in this article will allow us to investigate the effects of various realistic damage scenarios in long-span cable-supported (suspension and cable-stayed) bridges on changes in modal identification results. Such studies are required to develop robust and reliable vibration-based structural health monitoring methods for this type of bridge, which is a long-term research objective of the authors.     

Inventory lot-sizing with supplier selection using hybrid intelligent algorithm

In supply chain management (SCM), multi-product and multi-period models are usually used to select the suppliers. In the real world of SCM, however, there are normally several echelons which need to be integrated into inventory management. This paper presents a hybrid intelligent algorithm, based on the push SCM, which uses a fuzzy neural network and a genetic algorithm to forecast the rate of demand, determine the material planning and select the optimal supplier. We test the proposed algorithm in a case study conducted in Iran.     

Plasma Spray Deposition Enhancement of Titanium Nitride Layer Using Nitrogen-Contained Plasma Irradiation of Titanium Substrate as a Pre-spraying Method

Reactive plasma spray of TiN ceramic coating attracts much attention over the years because of its ability to deposit thick layers on various metal surfaces. However, some mechanical properties of the coating such as its hardness should be improved. In this study, initially a thin layer of titanium nitride was prepared on a titanium substrate during irradiation of titanium substrate by a thermal DC nitrogen-contained plasma jet. Then, during reactive plasma spraying, Ti particles were injected into plasma jet, converted to titanium nitride and huddled on to the substrate. This new hybrid method (primary plasma irradiation and post-reactive plasma spraying) for deposition of TiN coatings would combine the advantages of both plasma-enhanced chemical vapor deposition and reactive plasma spraying methods in part. It resulted in a thick and hard layer of titanium nitride film. Sample produced by this method was analyzed with x-ray diffraction confirming titanium nitride production. Vickers hardness was measured using optical microscopy which was around 1319 H_v300g. To study the cross section of the layer, optical microscopy and scanning electron microscopy (SEM) were used.     

Preparation,characterization and anticorrosive properties of a novel polyaniline/clinoptilolite nanocomposite

Nanocomposite of polyaniline (PANI) with natural clinoptilolite (Clino) was prepared. Formation of nanocomposite and incorporation of polyaniline in the clinoptilolite channels was confirmed and characterized using FTIR spectroscopy studies, X-ray diffraction (XRD) pattern, scanning electron microscopy (SEM) and cyclic voltammetry techniques. The anticorrosive properties of a 20 μm thickness coating of PANI/Clino nanocomposite with various weight ratios (1, 3 and 5%, w/w) of clinoptilolite content on iron coupons was evaluated and compared with pure polyaniline coating. According to the results in acidic environments PANI/Clino nanocomposite has enhanced corrosion protection effect in comparison to pure polyaniline coating. Comparative experiments revealed that PANI/Clino nanocomposite with 3% (w/w) clinoptilolite content has the best protective properties. Further experiments showed that the PANI/Clino nanocomposite has considerably different corrosion protection efficiencies in various corrosive environments.