Better means more: property rights and high-growth aspiration entrepreneurship

This paper contrasts the determinants of entrepreneurial entry and high-growth aspiration entrepreneurship. Using the Global Entrepreneurship Monitor (GEM) surveys for 42 countries over the period 1998-2005, we analyse how institutional environment and entrepreneurial characteristics affect individual decisions to become entrepreneurs and aspirations to set up high-growth ventures. We find that institutions exert different effects on entrepreneurial entry and on the individual choice to launch high-growth aspiration projects. In particular, a strong property rights system is important for high-growth aspiration entrepreneurship, but has less pronounced effects for entrepreneurial entry. The availability of finance and the fiscal burden matter for both

Machine learning for homogeneous grouping of pavements

Abstract Machine learning for homogeneous grouping of pavements. Kanan Mukhtarli Rapid pavement deterioration is a major problem in areas with harsh weather conditions or high traffic loading. Despite many studies focused on the pavement management systems, there is not, to the date, a robust method explaining how to process large amounts of pavement data to create homogeneous groups for rehabilitation-related decision making. This thesis employs machine learning to develop an approach capable of partitioning pavement data with a close response to casual factors like traffic and weather conditions and considering its performance through international roughness index and deflections. Two different methods: K-means and Self Organizing Maps (SOM) clustering techniques were tested to understand the correlation between daily factors and pavements deterioration. The goodness of clustering was tested using extrinsic and intrinsic evaluation methods. It was concluded from the results that SOM clustering provided better results as it relies on a soft clustering method where one point can represent two clusters at the same time. Moreover, it became obvious from the methodology that including the previous year’s data has very little to no effect on homogeneous groups. Techniques discussed and developed in this study can help road asset managers with decision making for the maintenance and rehabilitation of pavement. Moreover, future researchers can use the results of this study to further develop the idea of building decision support systems for pavement rehabilitation

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

The fourth dimension: A motoric perspective on the anxiety–performance relationship

This article focuses on raising concern that anxiety–performance relationship theory has insufficiently catered for motoric issues during, primarily, closed and self-paced skill execution (e.g., long jump and javelin throw). Following a review of current theory, we address the under-consideration of motoric issues by extending the three-dimensional model put forward by Cheng, Hardy, and Markland (2009) (‘Toward a three-dimensional conceptualization of performance anxiety: Rationale and initial measurement development, Psychology of Sport and Exercise, 10, 271–278). This fourth dimension, termed skill establishment, comprises the level and consistency of movement automaticity together with a performer's confidence in this specific process, as providing a degree of robustness against negative anxiety effects. To exemplify this motoric influence, we then offer insight regarding current theories’ misrepresentation that a self-focus of attention toward an already well-learned skill always leads to a negative performance effect. In doing so, we draw upon applied literature to distinguish between positive and negative self-foci and suggest that on what and how a performer directs their attention is crucial to the interaction with skill establishment and, therefore, performance. Finally, implications for skill acquisition research are provided. Accordingly, we suggest a positive potential flow from applied/translational to fundamental/theory-generating research in sport which can serve to freshen and usefully redirect investigation into this long-considered but still insufficiently understood concept

Better Means More: Property Rights and High-Growth Aspiration Entrepreneurship

This paper contrasts the determinants of entrepreneurial entry and high-growth aspiration entrepreneurship. Using the Global Entrepreneurship Monitor (GEM) surveys for 42 countries over the period 1998-2005, we analyse how institutional environment and entrepreneurial characteristics affect individual decisions to become entrepreneurs and aspirations to set up high-growth ventures. We find that institutions exert different effects on entrepreneurial entry and on the individual choice to launch high-growth aspiration projects. In particular, a strong property rights system is important for high-growth aspiration entrepreneurship, but has less pronounced effects for entrepreneurial entry. The availability of finance and the fiscal burden matter for both.entrepreneurship, high-growth aspiration entrepreneurship, start-ups, property rights

Opportunity and Access: High School Coursework and its Impact on Student Self-Perceptions

The skills taught and the courses offered to the United States’ public high school students have changed drastically over the past 20 years. Career-based electives have given way to college preparatory classes. Gone are the auto shops, construction and home economics classes—courses that many view as outdated, dangerous or unnecessary at the high school level. Arts and humanities-based courses have also disappeared; even courses that can train students to enter the robust fields of healthcare or technology are diminishing (Dare, 2006). The communities in which many of these courses are disappearing are often diverse and economically disadvantaged. This “second-generation segregation” which materializes as differentiated course offerings for students from various backgrounds may lend some insight into explaining the current inequities in student achievement (Southworth & Mickelson, 2007, p. 498). Perhaps students are disconnecting from their public education due to the loss of access to varied learning opportunities. The purpose of this mixed-method research study is to examine the impact of high school coursework on students’ perception of their post-secondary options. Current high school students and their school counselor are interviewed in an effort to determine if particular coursework, for better or worse, has any effect at all on a student’s self-perception and what they see as a feasible option for their future

Towards Robust Neural Networks via Random Self-ensemble

Recent studies have revealed the vulnerability of deep neural networks: A small adversarial perturbation that is imperceptible to human can easily make a well-trained deep neural network misclassify. This makes it unsafe to apply neural networks in security-critical applications. In this paper, we propose a new defense algorithm called Random Self-Ensemble (RSE) by combining two important concepts: {\bf randomness} and {\bf ensemble}. To protect a targeted model, RSE adds random noise layers to the neural network to prevent the strong gradient-based attacks, and ensembles the prediction over random noises to stabilize the performance. We show that our algorithm is equivalent to ensemble an infinite number of noisy models $f_\epsilon$ without any additional memory overhead, and the proposed training procedure based on noisy stochastic gradient descent can ensure the ensemble model has a good predictive capability. Our algorithm significantly outperforms previous defense techniques on real data sets. For instance, on CIFAR-10 with VGG network (which has 92\% accuracy without any attack), under the strong C\&W attack within a certain distortion tolerance, the accuracy of unprotected model drops to less than 10\%, the best previous defense technique has $48\%$ accuracy, while our method still has $86\%$ prediction accuracy under the same level of attack. Finally, our method is simple and easy to integrate into any neural network.Comment: ECCV 2018 camera read