On the von Neumann and Frank-Wolfe Algorithms with Away Steps

The von Neumann algorithm is a simple coordinate-descent algorithm to determine whether the origin belongs to a polytope generated by a finite set of points. When the origin is in the of the polytope, the algorithm generates a sequence of points in the polytope that converges linearly to zero. The algorithm's rate of convergence depends on the radius of the largest ball around the origin contained in the polytope. We show that under the weaker condition that the origin is in the polytope, possibly on its boundary, a variant of the von Neumann algorithm that includes generates a sequence of points in the polytope that converges linearly to zero. The new algorithm's rate of convergence depends on a certain geometric parameter of the polytope that extends the above radius but is always positive. Our linear convergence result and geometric insights also extend to a variant of the Frank-Wolfe algorithm with away steps for minimizing a strongly convex function over a polytope

ON THE ECONOMIC LINK BETWEEN ASSET PRICES AND REAL ACTIVITY

This paper presents a model linking two financial markets (stocks and bonds) with the real business cycle, in the framework of the Consumption Capital Asset Pricing Model with Generalized Isoelastic Preferences. Besides interest rate term spread, the model includes a new variable to forecast economic activity: stock market term spread, which constitutes the slope of expected stock market returns. The empirical evidence documented in this paper suggests systematic relationships between the state of the business cycle and the shapes of two yield curves (interest rates and expected stock returns). Results are robust to changes in measures of economic growth, stock prices, interest rates and expectation-generating mechanisms.

A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.Comment: 8 pages, 9 figures, International Conference for Data Intelligence and Security (ICDIS

A polymorphism in the stearoyl-CoA desaturase gene promoter influences monounsaturated fatty acid content of Duroc × Iberian hams

Data on 74 dry-cured hams from Duroc × Iberian pigs were used to examine whether the tag polymorphism AY487830:g.2228T>C in the promoter region of the stearoyl-CoA desaturase [SCD] gene affect fat desaturation and monounsaturated fatty acid (MUFA) as previously described in purebred Duroc hams. Samples were taken from sliced trays of dry-cured hams marketed as Jamón Ibérico de cebo, which were randomly purchased from the same supplier in different stores of the same supermarket chain. Genomic DNA was isolated from each sample to genotype for SCD and gender. Also, a sample of two slices was used to determine fat content and fatty acid (FA) composition by gas chromatography. The effect of the genotype (TT and CT) and gender (barrows and gilts) was estimated under a Bayesian setting. Results showed that the SCD polymorphism was associated to fat composition but not to fat content, with TT hams showing increased C18:1n-7, C18:1n-9, C20:1n-9 and MUFA (probability between 0.92-0.98) and decreased C18:2n-6, C20:4n-6 and polyunsaturated FA (PUFA) (probability between 0.91-0.99) as compared to the CT. As a result, the TT hams had more MUFA (0.95%) and a higher MUFA/PUFA ratio (0.43) than the CT. Barrows had more saturated FA (SFA) and less PUFA than gilts. No differences in MUFA content were found between genders. The SCD polymorphism had a greater impact on MUFA than using hams from barrows instead of gilts. It is concluded that the SCD polymorphism is a good tool to increase MUFA and MUFA/PUFA ratio in Duroc crossbred dry-cured hams.Funding: Spanish Ministry of Economy and Competitiveness (MINECO, grant AGL2012-33529). EHR is recipient of a PhD scholarship from the University of Lleid

A simple current control strategy for a four-leg indirect matrix converter

In this paper the experimental validation of a predictive current control strategy for a four-leg indirect matrix converter is presented. The four-leg indirect matrix converter can supply energy to an unbalanced three-phase load whilst providing a path for the zero sequence load. The predictive current control technique is based on the optimal selection among the valid switching states of the converter by evaluating a cost function, resulting in a simple approach without the necessity for modulators. Furthermore, zero dc-link current commutation is achieved by synchronizing the state changes in the input stage with the application of a zero voltage space vector in the inverter stage. Simulation results are presented and the strategy is experimentally validated using a laboratory prototype