Mixed Tree and Spatial Representation of Dissimilarity Judgments

Whereas previous research has shown that either tree or spatial representations of dissimilarity judgments may be appropriate, focussing on the comparative fit at the aggregate level, we investigate whether there is heterogeneity among subjects in the extent to which their dissimilarity judgments are better represented by ultrametric tree or spatial multidimensional scaling models. We develop a mixture model for the analysis of dissimilarity data, that is formulated in a stochastic context, and entails a representation and a measurement model component. The latter involves distributional assumptions on the measurement error, and enables estimation by maximum likelihood. The representation component allows dissimilarity judgments to be represented either by a tree structure or by a spatial configuration, or a mixture of both. In order to investigate the appropriateness of tree versus spatial representations, the model is applied to twenty empirical data sets. We compare the fit of our model with that of aggregate tree and spatial models, as well as with mixtures of pure trees and mixtures of pure spaces, respectively. We formulate some empirical generalizations on the relative importance of tree versus spatial structures in representing dissimilarity judgments at the individual level.Multidimensional scaling;tree models;mixture models;dissimilarity judgments

Methodological proposal for the characterization of accessions in Germplasm Banks using Generalized Procrustes Analysis applied to incomplete but connected trials

Characterization of plant material conserved in germplasm banks allows the study and analysis of the genetic variability within a collection. When germplasm banks have a large number of accessions, field evaluation should be performed using assays with manageable accession subsets. Common checks connecting the different assays are required to compare these accession subsets. In this study, the Generalized Procrustes Analysis was proposed as a basis for obtaining a factorial plane where all individuals are projected. This technique is applied to genotypes common to all assays, iteratively generating scale factors and rotation matrices. Accessions only belonging to a given assay are considered supplementary elements. This proposal was illustrated using datasets of 54 maize accessions from the Pergamino Active Germplasm Bank of the Experimental Station at the Instituto Nacional de Tecnología Agropecuaria (INTA) in Argentina. The proposal achieved highly satisfactory results.La caracterización del material vegetal conservado en bancos de germoplasma permite el estudio y análisis de la variabilidad genética dentro de una colección. Cuando los bancos de germoplasma tienen una gran cantidad de entradas, la evaluación de campo debe realizarse utilizando ensayos en los cuales se evalúa un subconjunto de poblaciones manejable experimentalmente. Se requieren poblaciones testigo que conecten los diferentes ensayos para comparar estos subconjuntos de accesiones. En este estudio, se propuso utilizar el Análisis de Procrustes Generalizado como base para obtener un plano factorial donde se proyectan todos los individuos. Esta técnica se aplica a los genotipos que son comunes a todos los ensayos, para generar iterativamente factores de escala y matrices de rotación. Las accesiones que solo pertenecen a un ensayo dado se consideran elementos suplementarios. La propuesta se ilustró utilizando un conjunto de datos de 54 accesiones de maíz del Banco Activo de Germoplasma Pergamino de la Estación Experimental del Instituto Nacional de Tecnología Agropecuaria de Argentina, donde se obtienen resultados altamente satisfactorios.EEA PergaminoFil: Lavalle, Andrea Lina. Universidad Nacional del Comahue. Departamento de Estadística; ArgentinaFil: Defacio, Raquel Alicia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Banco de Germoplasma; ArgentinaFil: De Leo, Mariano. Universidad Nacional de General Sarmiento. Instituto de Ciencias; ArgentinaFil: Bramardi, Sergio Jorge. Universidad Nacional del Comahue. Centro de Investigaciones en Toxicología Ambiental y Agrobiotecnología del Comahue (CONICET - UNCo).Departamento de Estadística; Argentin

Polyploidy breaks speciation barriers in Australian burrowing frogs Neobatrachus

Polyploidy has played an important role in evolution across the tree of life but it is still unclear how polyploid lineages may persist after their initial formation. While both common and well-studied in plants, polyploidy is rare in animals and generally less understood. The Australian burrowing frog genus Neobatrachus is comprised of six diploid and three polyploid species and offers a powerful animal polyploid model system. We generated exome-capture sequence data from 87 individuals representing all nine species of Neobatrachus to investigate species-level relationships, the origin and inheritance mode of polyploid species, and the population genomic effects of polyploidy on genus-wide demography. We describe rapid speciation of diploid Neobatrachus species and show that the three independently originated polyploid species have tetrasomic or mixed inheritance. We document higher genetic diversity in tetraploids, resulting from widespread gene flow between the tetraploids, asymmetric inter-ploidy gene flow directed from sympatric diploids to tetraploids, and isolation of diploid species from each other. We also constructed models of ecologically suitable areas for each species to investigate the impact of climate on differing ploidy levels. These models suggest substantial change in suitable areas compared to past climate, which correspond to population genomic estimates of demographic histories. We propose that Neobatrachus diploids may be suffering the early genomic impacts of climate-induced habitat loss, while tetraploids appear to be avoiding this fate, possibly due to widespread gene flow. Finally, we demonstrate that Neobatrachus is an attractive model to study the effects of ploidy on the evolution of adaptation in animals

Real-time Intrusion Detection using Multidimensional Sequence-to-Sequence Machine Learning and Adaptive Stream Processing

A network intrusion is any unauthorized activity on a computer network. There are host-based and network-based Intrusion Detection Systems (IDS\u27s), of which there are each signature-based and anomaly-based detection methods. An anomalous network behavior can be defined as an intentional violation of the expected sequence of packets. In a real-time network-based IDS, incoming packets are treated as a stream of data. A stream processor takes any stream of data or events and extracts interesting patterns on the fly. This representation allows applying statistical anomaly detection using sequence prediction algorithms as well as using a stream processor to perform signature-based intrusion detection and sequence extraction from a stream of packets. In this thesis, a Multidimensional Sequence to Multidimensional Sequence (MSeq2MSeq) encoder-decoder model is proposed to predict sequences of packets and an adaptive and functionally auto-scaling stream processor: Wisdom is proposed to process streams of packets. The proposed MSeq2MSeq model trained on legitimate traffic is able to detect Neptune Denial of Service (DoS) attacks, and Port Scan probes with 100% detection rate using the DARPA 1999 dataset. A hybrid algorithm using Particle Swarm Optimization (PSO) and Bisection algorithms was developed to optimize Complex Event Processing (CEP) rules in Wisdom . Adaptive CEP rules optimized by the above algorithm was able to detect FTP Brute Force attack, Slow Header DoS attack, and Port Scan probe with 100% detection rate while processing over 2.5 million events per second. An adaptive and functionally auto-scaling IDS was built using the MSeq2MSeq model and Wisdom stream processor to detect and prevent attacks based on anomalies and signature in real-time. The proposed IDS adapts itself to obtain best results without human intervention and utilizes available system resources in functionally auto-scaling deployment. Results show that the proposed IDS detects FTP Brute Force attack, Slow Header DoS attack, HTTP Unbearable Load King (HULK) DoS attack, SQL Injection attack, Web Brute Force attack, Cross-site scripting attack, Ares Botnet attack, and Port Scan probe with a 100% detection rate in a real-time environment simulated from the CICIDS 2017 dataset