MBIS: Multivariate Bayesian Image Segmentation Tool

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multi-channel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Mulch materials in processing tomato: a multivariate approach.

Mulch materials of different origins have been introduced into the agricultural sector in recent years alternatively to the standard polyethylene due to its environmental impact. This study aimed to evaluate the multivariate response of mulch materials over three consecutive years in a processing tomato (Solanum lycopersicon L.) crop in Central Spain. Two biodegradable plastic mulches (BD1, BD2), one oxo-biodegradable material (OB), two types of paper (PP1, PP2), and one barley straw cover (BS) were compared using two control treatments (standard black polyethylene [PE] and manual weed control [MW]). A total of 17 variables relating to yield, fruit quality, and weed control were investigated. Several multivariate statistical techniques were applied, including principal component analysis, cluster analysis, and discriminant analysis. A group of mulch materials comprised of OB and BD2 was found to be comparable to black polyethylene regarding all the variables considered. The weed control variables were found to be an important source of discrimination. The two paper mulches tested did not share the same treatment group membership in any case: PP2 presented a multivariate response more similar to the biodegradable plastics, while PP1 was more similar to BS and MW. Based on our multivariate approach, the materials OB and BD2 can be used as an effective, more environmentally friendly alternative to polyethylene mulches.

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

The multivariate L1-median and associated data depth

This paper gives three related results: (i) a new, simple, fast, monotonically converging algorithm for deriving the L1-median of a data cloud in ℝd, a problem that can be traced to Fermat and has fascinated applied mathematicians for over three centuries; (ii) a new general definition for depth functions, as functions of multivariate medians, so that different definitions of medians will, correspondingly, give rise to different dept functions; and (iii) a simple closed-form formula of the L1-depth function for a given data cloud in ℝd.

E-polynomial of the SL(3,C)-character variety of free groups.

We compute the E-polynomial of the character variety of representations of a rank r free group in SL(3,C). Expanding upon techniques of Logares, Muñoz and Newstead (Rev. Mat. Complut. 26:2 (2013), 635-703), we stratify the space of representations and compute the E-polynomial of each geometrically described stratum using fibrations. Consequently, we also determine the E-polynomial of its smooth, singular, and abelian loci and the corresponding Euler characteristic in each case. Along the way, we give a new proof of results of Cavazos and Lawton (Int. J. Math. 25:6 (2014), 1450058).

High-speed polynomial basis multipliers over GF(2(m)) for special pentanomial.

Efficient hardware implementations of arithmetic operations in the Galois field are highly desirable for several applications, such as coding theory, computer algebra and cryptography. Among these operations, multiplication is of special interest because it is considered the most important building block. Therefore, high-speed algorithms and hardware architectures for computing multiplication are highly required. In this paper, bit-parallel polynomial basis multipliers over the binary field GF(2(m)) generated using type II irreducible pentanomials are considered. The multiplier here presented has the lowest time complexity known to date for similar multipliers based on this type of irreducible pentanomials.

Comparación de las macroalgas epizóicas de la madreperla Pinctada mazatlanica (Hanley 1856) con las de fondos rocosos en la bahía de La Paz, Baja California Sur, México

El presente estudio describe la comunidad de macroalgas epizóicas de Pinctada mazatlanica y la compara con la del substrato rocoso. Colectamos un total de 36 muestras de la comunidad de macroalgas, 18 muestras sobre ostras y otras tantas sobre substrato rocoso en la Península San Juan Nepomuceno, bahía de La Paz, México. Las algas fueron colectadas mediante buceo SCUBA raspando las distintas superficies (25 cm2 de substrato rocoso). El tamaño de muestra fue ajustado por curva de acumulación de especies y expresado con un modelo polinomial. Comparamos las comunidades de algas con análisis multivariantes de similitud basados en el índice de Bray-Curtis, entre substratos, diferentes alturas de ostras perleras y profundidades. Encontramos 27 especies de algas epizóicas (15.4% Clorophyta, 3.8% Phaeophyta y 80.8% Rhodophyta) con una disimilitud de 71.16% con respecto al substrato rocoso. El dendrograma mostró tres agrupaciones de macroalgas en P. mazatlanica. El primero caracterizado por Chondria, Jania, Herposiphonia tenella y Gracilaria. El segundo compuesto por Jania, Polysiphonia acuminata, P. decusata y Spyridia filamentosa. El tercero constituido por Polysiphonia sp., Jania, Herposiphonia tenella, Ceramium canouii y Amphiroa sp. Estas agrupaciones y los talos filamentosos de las algas epizóicas corresponden a estados iniciales de sucesión.

Handling of Uncertainty in Life Cycle Inventory by Correlated Multivariate Lognormal Distributions: Application to the Design of Supply Chain Networks

Poster presented in the 24th European Symposium on Computer Aided Process Engineering (ESCAPE 24), Budapest, Hungary, June 15-18, 2014.

Handling of Uncertainty in Life Cycle Inventory by Correlated Multivariate Lognormal Distributions: Application to the Design of Supply Chain Networks

In this work, we analyze the effect of incorporating life cycle inventory (LCI) uncertainty on the multi-objective optimization of chemical supply chains (SC) considering simultaneously their economic and environmental performance. To this end, we present a stochastic multi-scenario mixed-integer linear programming (MILP) coupled with a two-step transformation scenario generation algorithm with the unique feature of providing scenarios where the LCI random variables are correlated and each one of them has the desired lognormal marginal distribution. The environmental performance is quantified following life cycle assessment (LCA) principles, which are represented in the model formulation through standard algebraic equations. The capabilities of our approach are illustrated through a case study of a petrochemical supply chain. We show that the stochastic solution improves the economic performance of the SC in comparison with the deterministic one at any level of the environmental impact, and moreover the correlation among environmental burdens provides more realistic scenarios for the decision making process.

Microwave-Assisted Extraction of Phenolic Compounds from Almond Skin Byproducts (Prunus amygdalus): A Multivariate Analysis Approach

A microwave-assisted extraction (MAE) procedure to isolate phenolic compounds from almond skin byproducts was optimized. A three-level, three-factor Box–Behnken design was used to evaluate the effect of almond skin weight, microwave power, and irradiation time on total phenolic content (TPC) and antioxidant activity (DPPH). Almond skin weight was the most important parameter in the studied responses. The best extraction was achieved using 4 g, 60 s, 100 W, and 60 mL of 70% (v/v) ethanol. TPC, antioxidant activity (DPPH, FRAP), and chemical composition (HPLC-DAD-ESI-MS/MS) were determined by using the optimized method from seven different almond cultivars. Successful discrimination was obtained for all cultivars by using multivariate linear discriminant analysis (LDA), suggesting the influence of cultivar type on polyphenol content and antioxidant activity. The results show the potential of almond skin as a natural source of phenolics and the effectiveness of MAE for the reutilization of these byproducts.

Multi-Element Analysis of Spanish Date Palm (Phoenix dactylifera L.) by Inductively Coupled Plasma-Based Techniques. Discrimination Using Multivariate Statistical Analysis

The elemental analysis of Spanish palm dates by inductively coupled plasma atomic emission spectrometry and inductively coupled plasma mass spectrometry is reported for the first time. To complete the information about the mineral composition of the samples, C, H, and N are determined by elemental analysis. Dates from Israel, Tunisia, Saudi Arabia, Algeria and Iran have also been analyzed. The elemental composition have been used in multivariate statistical analysis to discriminate the dates according to its geographical origin. A total of 23 elements (As, Ba, C, Ca, Cd, Co, Cr, Cu, Fe, H, In, K, Li, Mg, Mn, N, Na, Ni, Pb, Se, Sr, V, and Zn) at concentrations from major to ultra-trace levels have been determined in 13 date samples (flesh and seeds). A careful inspection of the results indicate that Spanish samples show higher concentrations of Cd, Co, Cr, and Ni than the remaining ones. Multivariate statistical analysis of the obtained results, both in flesh and seed, indicate that the proposed approach can be successfully applied to discriminate the Spanish date samples from the rest of the samples tested.

Coreferentiality: A New Method for the Hypothesis-Based Analysis of Phenotypes Characterized by Multivariate Data

Many multifactorial biologic effects, particularly in the context of complex human diseases, are still poorly understood. At the same time, the systematic acquisition of multivariate data has become increasingly easy. The use of such data to analyze and model complex phenotypes, however, remains a challenge. Here, a new analytic approach is described, termed coreferentiality, together with an appropriate statistical test. Coreferentiality is the indirect relation of two variables of functional interest in respect to whether they parallel each other in their respective relatedness to multivariate reference data, which can be informative for a complex effect or phenotype. It is shown that the power of coreferentiality testing is comparable to multiple regression analysis, sufficient even when reference data are informative only to a relatively small extent of 2.5%, and clearly exceeding the power of simple bivariate correlation testing. Thus, coreferentiality testing uses the increased power of multivariate analysis, however, in order to address a more straightforward interpretable bivariate relatedness. Systematic application of this approach could substantially improve the analysis and modeling of complex phenotypes, particularly in the context of human study where addressing functional hypotheses by direct experimentation is often difficult.

Multivariate statistical analysis of highway accident and highway conditions. Final report.

Transportation Department, Office of University Research, Washington, D.C.

SPORRT; simplified polynomial root finding at a remote terminal.

"Supported in part by ... Grant no. US NSF GP-9665."