Competitive learning and soft competition for vector quantizerdesign

The authors provide a convergence analysis for the Kohonen learning algorithm (KLA) with respect to vector quantizer (VQ) optimality criteria and introduce a stochastic relaxation technique which produces the global minimum but is computationally expensive. By incorporating the principles of the stochastic approach into the KLA, a deterministic VQ design algorithm, the soft competition scheme (SCS), is introduced. Experimental results are presented where the SCS consistently provided better codebooks than the generalized Lloyd algorithm (GLA), even when the same computation time was used for both algorithms. The SCS may therefore prove to be a valuable alternative to the GLA for VQ design     

Globally optimal vector quantizer design by stochastic relaxation

The authors present a unified formulation and study of vector quantizer design methods that couple stochastic relaxation (SR) techniques with the generalized Lloyd algorithm. Two new SR techniques are investigated and compared: simulated annealing (SA) and a reduced-complexity approach that modifies the traditional acceptance criterion for simulated annealing to an unconditional acceptance of perturbations. It is shown that four existing techniques all fit into a general methodology for vector quantizer design aimed at finding a globally optimal solution. Comparisons of the algorithms' performances when quantizing Gauss-Markov processes, speech, and image sources are given. The SA method is guaranteed to perform in a globally optimal manner, and the SR technique gives empirical results equivalent to those of SA. Both techniques result in significantly better performance than that obtained with the generalized Lloyd algorithm     

The impact of alkali elements on the degradation of CIGS solar cells

Unencapsulated CIGS solar cells with high and low contents of sodium (Na) and potassium (K) were simultaneously exposed to damp heat and illumination. The solar cells with a high alkali (Na, K) content exhibited higher initial conversion efficiencies, but degraded severely within 100 h, while the alkali poor samples kept relatively stable performance under damp heat and illumination. The degradation of the samples with a high alkali content resulted in the formation of sodium rich spots on the top ZnO:Al surface of the samples. This is likely caused by light‐induced Na⁺ migration via the grain boundaries in the absorber to the depletion region, where the Na⁺ accumulated. This allowed subsequent Na⁺ transport through the depletion region due to the lowering of the internal electric field caused both by the Na⁺ accumulation and illumination. The migration resulted in the formation of shunt paths, which reduced the shunt resistance and open circuit voltage. Furthermore, ingression of water into the ZnO:Al is expected to be responsible for a slow but steady increase in series resistance for both high and low alkali solar cells. Additionally, sodium migration led to a severe increase of the series resistance in case of alkali rich samples. Copyright © 2015 John Wiley & Sons, Ltd.     

The role of colloidal particles in the speciation and analysis of "dissolved" phosphorus

Colloidal-sized particles (1-1,000 nm) and high molecular mass material play an important, yet poorly understood role in the aqueous speciation of P. This study assessed the size distribution of P in 0.45-microm filtered soil solutions and soil-water extracts from three sandy soils (grassland, arable field and forest) using gel filtration chromatography (GFC) and membrane filtration (0.22- and 0.025-microm pore-size) and evaluated the impact of P speciation on colorimetric and ion-chromatographic methods for orthophosphate analysis. Between 40% and 58% of molybdate reactive P (MRP) and > 85% of molybdate unreactive P in the soil solution from the agricultural soils (pH 5.9-6.3) were associated with high molecular mass material (apparent size > 0.025 microm, or > 600 kDa on Superdex). In solutions from the forest soil (pH 3.2), high molecular mass P (HMMP) compounds were of minor importance (<8% of TP). The GFC elution profiles, composition and spectral characteristics of HMMP-containing solutions as well as the small relevance of HMMP at low pH were all indicative for associations between humic substances, Fe and/or Al, and P. Both MRP and ion chromatographic P measurements overestimated the free orthophosphate concentration (up to 2.3- and 1.4-fold, respectively) in 0.45-microm filtered HMMP-containing solutions. In 0.025-microm filtrates, free orthophosphate was the only MRP species present.     

Computational aspects of prospect theory with asset pricing applications

We develop an algorithm to compute asset allocations for Kahneman and Tversky’s (Econometrica, 47(2), 263–291, 1979) prospect theory. An application to benchmark data as in Fama and French (Journal of Financial Economics, 47(2), 427–465, 1992) shows that the equity premium puzzle is resolved for parameter values similar to those found in the laboratory experiments of Kahneman and Tversky (Econometrica, 47(2), 263–291, 1979). While previous studies like Benartzi and Thaler (The Quarterly Journal of Economics, 110(1), 73–92, 1995), Barberis, Huang and Santos (The Quarterly Journal of Economics, 116(1), 1–53, 2001), and Grüne and Semmler (Asset prices and loss aversion, Germany, Mimeo Bielefeld University, 2005) focussed on dynamic aspects of asset pricing but only used loss aversion to explain the equity premium puzzle our paper explains the unconditional moments of asset pricing by a static two-period optimization problem. However, we incorporate asymmetric risk aversion. Our approach allows reducing the degree of loss aversion from 2.353 to 2.25, which is the value found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992) while increasing the risk aversion from 1 to 0.894, which is a slightly higher value than the 0.88 found by Tversky and Kahneman (Journal of Risk and Uncertainty, 5, 297–323, 1992). The equivalence of these parameter settings is robust to incorporating the size and the value portfolios of Fama and French (Journal of Finance, 47(2), 427–465, 1992). However, the optimal prospect theory portfolios found on this larger set of assets differ drastically from the optimal mean-variance portfolio.     

Networks, Matroids, and Non-Shannon Information Inequalities

We define a class of networks, called matroidal networks, which includes as special cases all scalar-linearly solvable networks, and in particular solvable multicast networks. We then present a method for constructing matroidal networks from known matroids. We specifically construct networks that play an important role in proving results in the literature, such as the insufficiency of linear network coding and the unachievability of network coding capacity. We also construct a new network, from the Vamos matroid, which we call the Vamos network, and use it to prove that Shannon-type information inequalities are in general not sufficient for computing network coding capacities. To accomplish this, we obtain a capacity upper bound for the Vamos network using a non-Shannon-type information inequality discovered in 1998 by Zhang and Yeung, and then show that it is smaller than any such bound derived from Shannon-type information inequalities. This is the first application of a non-Shannon-type inequality to network coding. We also compute the exact routing capacity and linear coding capacity of the Vamos network. Finally, using a variation of the Vamos network, we prove that Shannon-type information inequalities are insufficient even for computing network coding capacities of multiple-unicast networks.     

Insufficiency of linear coding in network information flow

It is known that every solvable multicast network has a scalar linear solution over a sufficiently large finite-field alphabet. It is also known that this result does not generalize to arbitrary networks. There are several examples in the literature of solvable networks with no scalar linear solution over any finite field. However, each example has a linear solution for some vector dimension greater than one. It has been conjectured that every solvable network has a linear solution over some finite-field alphabet and some vector dimension. We provide a counterexample to this conjecture. We also show that if a network has no linear solution over any finite field, then it has no linear solution over any finite commutative ring with identity. Our counterexample network has no linear solution even in the more general algebraic context of modules, which includes as special cases all finite rings and Abelian groups. Furthermore, we show that the network coding capacity of this network is strictly greater than the maximum linear coding capacity over any finite field (exactly 10% greater), so the network is not even asymptotically linearly solvable. It follows that, even for more general versions of linearity such as convolutional coding, filter-bank coding, or linear time sharing, the network has no linear solution.     

Stochastic relaxation algorithm for improved vector quantiser design

An easily implementable stochastic relaxation algorithm for vector quantisation design is given. It generalises the usual Lloyd iteration in codebook design by perturbing the computed centroids with independent multidimensional noise, whose variance diminishes as the algorithm progresses. A significant improvement is often achieved.<>     

Learning and design of principal curves

Principal curves have been defined as “self-consistent” smooth curves which pass through the “middle” of a d-dimensional probability distribution or data cloud. They give a summary of the data and also serve as an efficient feature extraction tool. We take a new approach by defining principal curves as continuous curves of a given length which minimize the expected squared distance between the curve and points of the space randomly chosen according to a given distribution. The new definition makes it possible to theoretically analyze principal curve learning from training data and it also leads to a new practical construction. Our theoretical learning scheme chooses a curve from a class of polygonal lines with k segments and with a given total length to minimize the average squared distance over n training points drawn independently. Convergence properties of this learning scheme are analyzed and a practical version of this theoretical algorithm is implemented. In each iteration of the algorithm, a new vertex is added to the polygonal line and the positions of the vertices are updated so that they minimize a penalized squared distance criterion. Simulation results demonstrate that the new algorithm compares favorably with previous methods, both in terms of performance and computational complexity, and is more robust to varying data models     

Kuwanon‐L as a New Allosteric HIV‐1 Integrase Inhibitor: Molecular Modeling and Biological Evaluation

HIV‐1 integrase (IN) active site inhibitors are the latest class of drugs approved for HIV treatment. The selection of IN strand‐transfer drug‐resistant HIV strains in patients supports the development of new agents that are active as allosteric IN inhibitors. Here, a docking‐based virtual screening has been applied to a small library of natural ligands to identify new allosteric IN inhibitors that target the sucrose binding pocket. From theoretical studies, kuwanon‐L emerged as the most promising binder and was thus selected for biological studies. Biochemical studies showed that kuwanon‐L is able to inhibit the HIV‐1 IN catalytic activity in the absence and in the presence of LEDGF/p75 protein, the IN dimerization, and the IN/LEDGF binding. Kuwanon‐L also inhibited HIV‐1 replication in cell cultures. Overall, docking and biochemical results suggest that kuwanon‐L binds to an allosteric binding pocket and can be considered an attractive lead for the development of new allosteric IN antiviral agents.