Multi-level visualisation using Gaussian process latent variable models

Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the ‘Oil Flow’ dataset and a dataset of protein electrostatic potentials for the ‘Major Histocompatibility Complex (MHC) class I’ of humans. In both cases, visual observation and the quantitative quality measures have shown better visualisation at lower levels.

Discontinuous control of multi-variable processes

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

The upstream Variable Number Tandem Repeat polymorphism of the monoamine oxidase type A gene influences trigeminal pain-related evoked responses

Monoamines have an important role in neural plasticity, a key factor in cortical pain processing that promotes changes in neuronal network connectivity. Monoamine oxidase type A (MAOA) is an enzyme that, due to its modulating role in monoaminergic activity, could play a role in cortical pain processing. The X-linked MAOA gene is characterized by an allelic variant of length, the MAOA upstream Variable Number Tandem Repeat (MAOA-uVNTR) region polymorphism. Two allelic variants of this gene are known, the high-activity MAOA (HAM) and low-activity MAOA (LAM). We investigated the role of MAOA-uVNTR in cortical pain processing in a group of healthy individuals measured by the trigeminal electric pain-related evoked potential (tPREP) elicited by repeated painful stimulation. A group of healthy volunteers was genotyped to detect MAOA-uVNTR polymorphism. Electrical tPREPs were recorded by stimulating the right supraorbital nerve with a concentric electrode. The N2 and P2 component amplitude and latency as well as the N2-P2 inter-peak amplitude were measured. The recording was divided into three blocks, each containing 10 consecutive stimuli and the N2-P2 amplitude was compared between blocks. Of the 67 volunteers, 37 were HAM and 30 were LAM. HAM subjects differed from LAM subjects in terms of amplitude of the grand-averaged and first-block N2-P2 responses (HAM>LAM). The N2-P2 amplitude decreased between the first and third block in HAM subjects but not LAM subjects. The MAOA-uVNTR polymorphism seemed to influence the brain response in a repeated tPREP paradigm and suggested a role of the MAOA as a modulator of neural plasticity related to cortical pain processing. Monoamines have an important role in neural plasticity, a key factor in cortical pain processing that promotes changes in neuronal network connectivity. Monoamine oxidase type A (MAOA) is an enzyme that, due to its modulating role in monoaminergic activity, could play a role in cortical pain processing. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

The influence of socio-cultural environments on the performance of nascent entrepreneurs:an instrumental variable approach

The importance of informal institutions and in particular culture for entrepreneurship is a subject of ongoing interest. Past research has mostly concentrated on cross-national comparisons, cultural values, and the direct effects of culture on entrepreneurial behavior, but in the main found inconsistent results. The present research adds a fresh perspective to this research stream by turning attention to community-level culture and cultural norms. We hypothesize indirect effects of cultural norms on venture emergence. Specifically that community-level cultural norms (performance-based culture and socially-supportive institutional norms) impact important supply-side variables (entrepreneurial self-efficacy and entrepreneurial motivation) which in turn influence nascent entrepreneurs’ success in creating operational ventures (venture emergence). We test our predictions on a unique longitudinal data set (PSED II) tracking nascent entrepreneurs venture creation efforts over a 5 year time span and find evidence supporting them. Our research contributes to a more fine-grained understanding of how culture, in particular perceptions of community cultural norms, influences venture emergence. This research highlights the embeddedness of entrepreneurial behavior and its immediate antecedent beliefs in the local, community context.

Null Condition for Semilinear Wave Equation with Variable Coefficients

∗The author was partially supported by M.U.R.S.T. Progr. Nazionale “Problemi Non Lineari...”

Fluid, heat and mass transfer in a Venlo type greenhouse with variable boundary conditions

DUE TO COPYRIGHT RESTRICTIONS, ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY WITH PRIOR ARRANGEMENT

Multidimensional Heterogeneous Variable Prediction Based on Experts’ Statements

* The work was supported by the RFBR under Grant N07-01-00331a.

An Approach to Variable Aggregation in Efficiency Analysis

In the nonparametric framework of Data Envelopment Analysis the statistical properties of its estimators have been investigated and only asymptotic results are available. For DEA estimators results of practical use have been proved only for the case of one input and one output. However, in the real world problems the production process is usually well described by many variables. In this paper a machine learning approach to variable aggregation based on Canonical Correlation Analysis is presented. This approach is applied for efficiency estimation of all the farms in Terceira Island of the Azorean archipelago.

On Coordination of Experts’ Estimations of Quantitative Variable

* The work was supported by the RFBR under Grants N07-01-00331a, 08-07-00136a

Hardy Inequality in Variable Exponent Lebesgue Spaces

Mathematics Subject Classification: 26D10, 46E30, 47B38

On a Class of Fractional Type Integral Equations in Variable Exponent Spaces

2000 Mathematics Subject Classification: 45A05, 45B05, 45E05,45P05, 46E30

Method for Analytical Representation of the Maximum Inaccuracies of Indirectly Measurable Variable

Let us have an indirectly measurable variable which is a function of directly measurable variables. In this survey we present the introduced by us method for analytical representation of its maximum absolute and relative inaccuracy as functions, respectively, of the maximum absolute and of the relative inaccuracies of the directly measurable variables. Our new approach consists of assuming for fixed variables the statistical mean values of the absolute values of the coefficients of influence, respectively, of the absolute and relative inaccuracies of the directly measurable variables in order to determine the analytical form of the maximum absolute and relative inaccuracies of an indirectly measurable variable. Moreover, we give a method for determining the numerical values of the maximum absolute and relative inaccuracies. We define a sample plane of the ideal perfectly accurate experiment and using it we give a universal numerical characteristic – a dimensionless scale for determining the quality (accuracy) of the experiment.

Solving the Task Assignment Problem with a Variable Neighborhood Search

In this paper a variable neighborhood search (VNS) approach for the task assignment problem (TAP) is considered. An appropriate neighborhood scheme along with a shaking operator and local search procedure are constructed specifically for this problem. The computational results are presented for the instances from the literature, and compared to optimal solutions obtained by the CPLEX solver and heuristic solutions generated by the genetic algorithm. It can be seen that the proposed VNS approach reaches all optimal solutions in a quite short amount of computational time.