Topics in inference and decision-making with partial knowledge

Two essential elements needed in the process of inference and decision-making are prior probabilities and likelihood functions. When both of these components are known accurately and precisely, the Bayesian approach provides a consistent and coherent solution to the problems of inference and decision-making. In many situations, however, either one or both of the above components may not be known, or at least may not be known precisely. This problem of partial knowledge about prior probabilities and likelihood functions is addressed. There are at least two ways to cope with this lack of precise knowledge: robust methods, and interval-valued methods. First, ways of modeling imprecision and indeterminacies in prior probabilities and likelihood functions are examined; then how imprecision in the above components carries over to the posterior probabilities is examined. Finally, the problem of decision making with imprecise posterior probabilities and the consequences of such actions are addressed. Application areas where the above problems may occur are in statistical pattern recognition problems, for example, the problem of classification of high-dimensional multispectral remote sensing image data

THE DIFFERENTIAL IMPACT OF CORRUPTION ON MICROENTERPRISES IN RUSSIA

Over the past decade, the repressive legal and regulatory environment in transition economies has received considerable attention in the literature. In Russia, this framework has resulted in an environment in which rules and regulations govern almost all aspects of economic activity. The elaborate system of regulations with which firms must comply, in combination with a lack of accountability for regulatory enforcers, has created a corrupt cadre of government officials who frequently engage in rent-seeking behavior while monitoring and enforcing firm compliance. The objective of this paper is to investigate the manner in which corruption affects micro and small enterprises in Russia. Empirical evidence suggests that micro and small enterprises vary substantially in reporting how problematic corruption is for their enterprise. A theoretical model explores why extortion from regulators may occur in a non-uniform manner across firms. The theoretical model postulates that government regulators customize the nature of their rent-seeking activities towards, similar to a price-discriminating monopolist facing hidden information. The model shows that production technologies, input choices, and other firm characteristics such as location play a role in determining the bribe price that a regulator will charge a firm, as well as the number of times he will return to collect it. Supportive evidence comes from survey data collected on Russian microenterprises. The model described above is tested using econometrics, and numerical simulations.Political Economy,

Traditional Gender Role Beliefs and Career Attainment in STEM: A Gendered Story?

Gender role beliefs (i.e., beliefs about gender-specific responsibilities) predict one’s educational and occupational aspirations and choices (Eccles et al., 1983; Schoon and Parsons, 2002). Focusing on STEM careers, we aim to examine the extent to which traditional work/family related gender role beliefs (TGRB) in adolescence predict within and across gender differences in subsequent educational and STEM occupational attainment in adulthood. Using longitudinal data from the Michigan Study of Adolescent and Adult Life Transitions (N = 744; 58% female), participants’ educational attainment and their occupations were assessed at age 42. Their occupations were then categorized into three categories: traditional STEM-related careers in the physical sciences, mathematics, engineering, and technology (PMET); life sciences (e.g., health sciences, LS); and non-STEM. For females, TGRB at age 16/18 significantly predicted lower educational attainment as well as a lower likelihood to be in PMET-related occupations in comparison to non-STEM occupations – controlling for their own educational attainment. TGRB also predicted a higher likelihood to be in LS-related in comparison to PMET-related occupations. No significant associations were found for males. However, patterns of findings for males were similar to those of females. TGRB also mediated across gender differences in educational and PMET-related occupational attainment. Findings reveal TGRB to be one underlying psychological factor influencing gender disparity in educational and STEM occupational attainment

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

Application of fuzzy logic to assess the quality of BPMN models

© Springer International Publishing AG, part of Springer Nature 2018. Modeling is the first stage in a Business Process’s (BP) lifecycle. A high-quality BP model is vital to the successful implementation, execution, and monitoring stages. Different works have evaluated BP models from a quality perspective. These works either used formal verification or a set of quality metrics. This paper adopts quality metric and targets models represented in Business Process Modeling and Notation (BPMN). It proposes an approach based on fuzzy logic along with a tool system developed under eclipse framework. The preliminary experimental evaluation of the proposed system shows encouraging results

Investigations into a putative role for the novel BRASSIKIN pseudokinases in compatible pollen-stigma interactions in Arabidopsis thaliana.

BACKGROUND: In the Brassicaceae, the early stages of compatible pollen-stigma interactions are tightly controlled with early checkpoints regulating pollen adhesion, hydration and germination, and pollen tube entry into the stigmatic surface. However, the early signalling events in the stigma which trigger these compatible interactions remain unknown. RESULTS: A set of stigma-expressed pseudokinase genes, termed BRASSIKINs (BKNs), were identified and found to be present in only core Brassicaceae genomes. In Arabidopsis thaliana Col-0, BKN1 displayed stigma-specific expression while the BKN2 gene was expressed in other tissues as well. CRISPR deletion mutations were generated for the two tandemly linked BKNs, and very mild hydration defects were observed for wild-type Col-0 pollen when placed on the bkn1/2 mutant stigmas. In further analyses, the predominant transcript for the stigma-specific BKN1 was found to have a premature stop codon in the Col-0 ecotype, but a survey of the 1001 Arabidopsis genomes uncovered three ecotypes that encoded a full-length BKN1 protein. Furthermore, phylogenetic analyses identified intact BKN1 orthologues in the closely related outcrossing Arabidopsis species, A. lyrata and A. halleri. Finally, the BKN pseudokinases were found to be plasma-membrane localized through the dual lipid modification of myristoylation and palmitoylation, and this localization would be consistent with a role in signaling complexes. CONCLUSION: In this study, we have characterized the novel Brassicaceae-specific family of BKN pseudokinase genes, and examined the function of BKN1 and BKN2 in the context of pollen-stigma interactions in A. thaliana Col-0. Additionally, premature stop codons were identified in the predicted stigma specific BKN1 gene in a number of the 1001 A. thaliana ecotype genomes, and this was in contrast to the out-crossing Arabidopsis species which carried intact copies of BKN1. Thus, understanding the function of BKN1 in other Brassicaceae species will be a key direction for future studies

Fall Detection with Unobtrusive Infrared Array Sensors

As the world’s aging population grows, fall is becoming a major problem in public health. It is one of the most vital risks to the elderly. Many technology based fall detection systems have been developed in recent years with hardware ranging from wearable devices to ambience sensors and video cameras. Several machine learning based fall detection classifiers have been developed to process sensor data with various degrees of success. In this paper, we present a fall detection system using infrared array sensors with several deep learning methods, including long-short-term-memory and gated recurrent unit models. Evaluated with fall data collected in two different sets of configurations, we show that our approach gives significant improvement over existing works using the same infrared array sensor