Literature-based priors for gene regulatory networks

Motivation: The use of prior knowledge to improve gene regulatory network modelling has often been proposed. In this paper we present the first research on the massive incorporation of prior knowledge from literature for Bayesian network learning of gene networks. As the publication rate of scientific papers grows, updating online databases, which have been proposed as potential prior knowledge in past rese-arch, becomes increasingly challenging. The novelty of our approach lies in the use of gene-pair association scores that describe the over-lap in the contexts in which the genes are mentioned, generated from a large database of scientific literature, harnessing the information contained in a huge number of documents into a simple, clear format. Results: We present a method to transform such literature-based gene association scores to network prior probabilities, and apply it to learn gene sub-networks for yeast, E. coli and Human organisms. We also investigate the effect of weighting the influence of the prior know-ledge. Our findings show that literature-based priors can improve both the number of true regulatory interactions present in the network and the accuracy of expression value prediction on genes, in comparison to a network learnt solely from expression data. Networks learnt with priors also show an improved biological interpretation, with identified subnetworks that coincide with known biological pathways. Contact

IMPACT OF INSTITUTION-COMMUNITY ENTREPRENEURSHIP EDUCATION ECOSYSTEM (ICEEE) ON BUSINESS EDUCATION GRADUATES’ JOBS CREATION IN OMOKU – RIVERS STATE, NIGERIA FROM 2017 TO 2019

This study examines the impact of institution-community entrepreneurship education ecosystem (ICEEE) on business education graduates’ jobs creation skill. A descriptive follow up study of 329 Business education degree graduate students in the year 2017 and 2018, using a questionnaire, mean, cluster mean of the responses, and multiple regression analysis revealed that the students learned product-production using the model as well-established small-scale businesses; although not many jobs were created. The effectiveness of the ICEEE model can be experimented in different fields of study to equip students with requisite knowledge and skills for self-reliance.  Article visualizations

Mean semi-deviation from a target and robust portfolio choice under distribution and mean return ambiguity

Cataloged from PDF version of article.We consider the problem of optimal portfolio choice using the lower partial moments risk measure for a market consisting of n risky assets and a riskless asset. For when the mean return vector and variance/covariance matrix of the risky assets are specified without specifying a return distribution, we derive distributionally robust portfolio rules. We then address potential uncertainty (ambiguity) in the mean return vector as well, in addition to distribution ambiguity, and derive a closed-form portfolio rule for when the uncertainty in the return vector is modelled via an ellipsoidal uncertainty set. Our result also indicates a choice criterion for the radius of ambiguity of the ellipsoid. Using the adjustable robustness paradigm we extend the single-period results to multiple periods, and derive closed-form dynamic portfolio policies which mimic closely the single-period policy. © 2013 Elsevier B.V. All rights reserved

Integrating E-Learning Technologies in Business Education Course Delivery During Covid-19 Lockdown in South-South Nigeria

This study investigates the acceptability of utilizing e-learning technologies for business education course-delivery during the period of tertiary institutions lockdown as a result of COVID-19 pandemic in south-south, Nigeria. Hinged on the theory of Technology Acceptance by Davis, F. D, a descriptive survey research design using quantitative and qualitative approach was adopted. Data was collected via questionnaire, and Focus Group Discussion (FGD) was used for the qualitative approach. The study revealed that business educators utilized e-learning technologies to a very low extent during the pandemic. Therefore, strategies to forestall such hindrances and difficulties were identified and discussed. Keywords: e-learning, academic staff attitudes, ICT, synchronous, asynchronous, COVID-19 DOI: 10.7176/JEP/11-32-09 Publication date: November 30th 202

Porometry, porosimetry, image analysis and void network modelling in the study of the pore-level properties of filters

We present fundamental and quantitative comparisons between the techniques of porometry (or flow permporometry), porosimetry, image analysis and void network modelling for seven types of filter, chosen to encompass the range of simple to complex void structure. They were metal, cellulose and glass fibre macro- and meso-porous filters of various types. The comparisons allow a general re-appraisal of the limitations of each technique for measuring void structures. Porometry is shown to give unrealistically narrow void size distributions, but the correct filtration characteristic when calibrated. Shielded mercury porosimetry can give the quaternary (sample-level anisotropic) characteristics of the void structure. The first derivative of a mercury porosimetry intrusion curve is shown to underestimate the large number of voids, but this error can be largely corrected by the use of a void network model. The model was also used to simulate the full filtration characteristic of each sample, which agreed with the manufacturer's filtration ratings. The model was validated through its correct a priori simulation of absolute gas permeabilities for track etch, cellulose nitrate and sintered powder filters. © 2011 Elsevier Ltd

Robust portfolio choice with CVaR and VaR under distribution and mean return ambiguity

Cataloged from PDF version of article.We consider the problem of optimal portfolio choice using the Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR) measures for a market consisting of n risky assets and a riskless asset and where short positions are allowed. When the distribution of returns of risky assets is unknown but the mean return vector and variance/covariance matrix of the risky assets are fixed, we derive the distributionally robust portfolio rules. Then, we address uncertainty (ambiguity) in the mean return vector in addition to distribution ambiguity, and derive the optimal portfolio rules when the uncertainty in the return vector is modeled via an ellipsoidal uncertainty set. In the presence of a riskless asset, the robust CVaR and VaR measures, coupled with a minimum mean return constraint, yield simple, mean-variance efficient optimal portfolio rules. In a market without the riskless asset, we obtain a closed-form portfolio rule that generalizes earlier results, without a minimum mean return restriction

EMPIRICAL EVIDENCE ON THE INFLUENCE OF EXPERIENTIAL INSTRUCTIONAL DESIGN LEARNING ACTIVITIES ON BUSINESS EDUCATION STUDENTS’ E-BUSINESS COMPETENCY DEVELOPMENT

This study examined the influence of experiential instructional design learning activities proposed by Abdulkarim and Ordu (2018) on business education students’ electronic business (e-business) competencies development in Colleges of Education in Rivers State. To achieve the main purpose of the study, two specific purposes, two research questions and one hypothesis were posed. The study adopted a descriptive survey research design to obtain data on participants’ self-evaluation. The population of the study was made up of 22 part-time Business education NCE I students from Federal College of Education (Tech.), Omoku during the 2018/2019 academic session. A research designed questionnaire was used to collect data for the study. The reliability of the instrument was determined using Cronbach alpha to obtain a reliability index of 0.72. Data collected for the study were analysed using mean and simple linear regression. The findings show that the students highly develop e-business competencies evaluated. The result of test of hypothesis showed that experiential instructional design learning activities has significant influence on business education students’ development of e-business competencies. Based on the findings, it was recommended among others business educators who want to bridge the gap should adopt experiential instructional design.  Article visualizations

Short-sales constraints and the diversification puzzle

Disagreement about stock valuation, combined with short-sales constraints, can increase asset prices. We build a model showing that, so long as investor beliefs are not perfectly correlated, investors will disagree less about the value of a conglomerate than about each of its individual divisions. This generates a conglomerate discount, with disagreement and short-sales constraints being complementary in explaining its cross-sectional variation. We test these predictions empirically and find substantial support: conglomerates have lower differences of opinion and lower short-sales constraints than pure-play firms. Furthermore, greater differences of opinion and tighter short-sales constraints are significant predictors of valuation differences between conglomerates and pure plays.Cambridge Endowment for Research in Finance (CERF

The Creative Class and the Creative Economy in Spain

This article describes an application in Spain of Florida's model (2002/Citation2010, Citation2005) about creativity, economy and growth. Creativity is an indicator that measures and combines technology, talent, and tolerance. Each of these is composed of three subindices. The most important conclusion from the data reported here is that creativity in particular, and growth in general, was less related to tolerance than the other two indices. However, the subindex of tolerance reflecting bohemia was important; the other two (foreigners and gays) were not

Flow-based detection and proxy-based evasion of encrypted malware C2 traffic

State of the art deep learning techniques are known to be vulnerable to evasion attacks where an adversarial sample is generated from a malign sample and misclassified as benign. Detection of encrypted malware command and control traffic based on TCP/IP flow features can be framed as a learning task and is thus vulnerable to evasion attacks. However, unlike e.g. in image processing where generated adversarial samples can be directly mapped to images, going from flow features to actual TCP/IP packets requires crafting the sequence of packets, with no established approach for such crafting and a limitation on the set of modifiable features that such crafting allows. In this paper we discuss learning and evasion consequences of the gap between generated and crafted adversarial samples. We exemplify with a deep neural network detector trained on a public C2 traffic dataset, white-box adversarial learning, and a proxy-based approach for crafting longer flows. Our results show 1) the high evasion rate obtained by using generated adversarial samples on the detector can be significantly reduced when using crafted adversarial samples; 2) robustness against adversarial samples by model hardening varies according to the crafting approach and corresponding set of modifiable features that the attack allows for; 3) incrementally training hardened models with adversarial samples can produce a level playing field where no detector is best against all attacks and no attack is best against all detectors, in a given set of attacks and detectors. To the best of our knowledge this is the first time that level playing field feature set- and iteration-hardening are analyzed in encrypted C2 malware traffic detection.Comment: 9 pages, 6 figure