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Experimental results from student and other non-representative convenience samples often suggest that men, on average, are more risk taking and competitive than women. We explore whether these gender preference gaps also exist in incentivized tasks in a simple random sample of the Swedish adult population. Our design comprises four different conditions to systematically explore how the experimenta
Semiconductor double quantum dots (DQD) coupled to superconducting microwave resonators offer a promising platform for the detection of single microwave photons. In previous works, the photodetection was studied for a monochromatic source of microwave photons. Here, we theoretically analyze the photodetection of single microwave pulses. The photodetection in this case can be seen as a nonlinear fi
The emergency department is a stressful environment, in which physicians are required to make fast and accurate diagnostic assessments amidst an ever increasing flood of clinical information, including a growing body of medical knowledge. Meanwhile, the digitization of medical health records in combination with recent breakthroughs in Artificial Intelligence is ushering in a new era of precision m
Random indexing (RI) is a lightweight dimension reduction method, which is used, for example, to approximate vector semantic relationships in online natural language processing systems. Here we generalise RI to multidimensional arrays and therefore enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections, which
The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineeri
The traditional way of teaching may no longer be sufficient to cope with current requirements specifically in the Internet of Things (IoT) domain. The case for this paper is related to an introductory programming course on JavaScript for the period of 2016-2018. In this study a multi-method approach for data collection is utilized. Project-Based Learning (PBL), Scrum and rapid prototyping are util
The pyISC is a Python API and extension to the C++ based Incremental Stream Clustering (ISC) anomaly detection and classification framework. The framework is based on parametric Bayesian statistical inference using the Bayesian Principal Anomaly (BPA), which enables to combine the output from several probability distributions. pyISC is designed to be easy to use and integrated with other Python li
IntroductionImmigrant children are often challenged at school. School performance is an important predictor of future socioeconomic position and mental and physical health. While studies have investigated parental mental disorders as a potential factor for poor school performance, no studies have investigated this among children with foreign-born parents, i.e., second-generation immigrant children
This CMES Regional Outlook by Pinar Dinc focuses on the recent political developments in Turkey.
The classic statistical method for modelling the rates and proportions is the beta regression model (BRM). The standard maximum likelihood estimator (MLE) is used to estimate the coefficients of the BRM. However, this MLE is very sensitive when the regressors are linearly correlated. Therefore, this study introduces a new beta ridge regression (BRR) estimator as a remedy to the problem of instabil
This paper considers the estimation of parameters for the Poisson regression model in the presence of high, but imperfect multicollinearity. To mitigate this problem, we suggest using the Poisson Liu Regression Estimator (PLRE) and propose some new approaches to estimate this shrinkage parameter. The small sample statistical properties of these estimators are systematically scrutinized using Monte
Due to the large amount of accidents negatively affecting the wellbeing of the survivors and their families, a substantial amount of research is conducted to determine the causes of road accidents. This type of data come in the form of non-negative integers and may be modelled using the Poisson regression model. Unfortunately, the commonly used maximum likelihood estimator is unstable when the exp
