Academic writing | EPFL Graph Search

Academic writing or scholarly writing is nonfiction writing produced as part of academic work in accordance with the standards and disciplines of each academic subject, including:

Reports on empirical fieldwork or research in facilities for the natural sciences or social sciences,
Monographs in which scholars analyze culture, propose new theories, or
Develop interpretations from archives,

as well as undergraduate versions of all of these. Though the tone, style, content, and organization of academic writing vary across genres and across publication methods, nearly all academic writing shares a relatively formal prose register, frequent reference to other academic work, and the use of fairly stable rhetorical moves to define the scope of the project, situate it in the relevant research, and to advance a new contribution. Academic style Academic writing often features prose register that is conventionally characterized by "evidence...that the writer(s) have been

Institutions and Incentives in Knowledge Production and Diffusion: From Science to Innovation

Charles Chadi Ayoubi

This thesis presents four essays providing novel empirical and theoretical insights on the incentives and institutional structures that favor knowledge production and diffusion. The first two studies analyze these processes in the realm of scientific research, while the last two essays evaluate broader applications with vast social welfare implications. The first essay (chapter 2) of this dissertation, in collaboration with Michele Pezzoni and Fabiana Visentin, exploits a dataset on all applicants to a prestigious Swiss grant to explore a central process in academic life: the application for funds. The results suggest that scientists applying to a grant significantly increase their publicationsâ quality and quantity, learn more, and extend their collaboration network. Beyond the effect of applying, receiving the research funds increases the probability of co-authoring with co-applicants, but it does not have any additional effect on other scientific outcomes. These results justified the title of the chapter since, as it is the case in the Olympics, in research grants, âthe important thing is not to win, it is to take part.â The second essay (chapter 3), also in collaboration with Michele Pezzoni and Fabiana Visentin, uses the same empirical context to explore the determinants of knowledge flows among collaborating scientists. The chapter proposes a novel methodology based on journal references to track knowledge flows among researchers working together. The results suggest that geographical distance does not significantly affect the knowledge flows between team members, but the cognitive distance separating two members does. More specifically, there is an inverted U-curve effect of cognitive distance on the learning among team members: the higher the distance between two scientists in terms of subjects studied, the more they exchange knowledge, up to the point when the distance becomes detrimental because they have too little common ground to communicate. The third essay (chapter 4), in collaboration with Boris Thurm, goes beyond the exploration of the determinants for scientistsâ knowledge production and diffusion to delve into the incentives of all individuals to exchange knowledge. The chapter has two major contributions. First, it acts as a literature review of the empirical evidence on non-financial incentives for knowledge diffusion, such as social recognition, career prospects, and moral considerations. Second, the chapter proposes a simple economic model with heterogeneous agents holding both selfish and moral motives to derive some novel policy implications. The last essay (chapter 5), in collaboration with Dominique Foray, delves into a specific case of knowledge diffusion, the integration of machine learning technologies in healthcare. The analysis suggests that machine learning has the potential for spurring innovation in healthcare but faces several institutional levers. Collecting quantitative data on patents and publications, and qualitative data on hospitals, the results show that machine learning affects healthcare in different ways than older information and communication technologies. The apparition of new business models encourages tech giants to enter the healthcare sector. These patterns have the potential to increase social welfare by reducing externalities in terms of innovation complementarities, but they pose new challenges such as competition policy and human capital formation.

EPFL2020

Classification of fall directions via wearable motion sensors

Mustafa Sahin Turan

Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of academic work on this class of systems, not much effort has been devoted to the investigation of effective and robust algorithms and like-for-like comparison of state-ofthe-art algorithms using a sufficiently large dataset. In this article, fall-direction classification algorithms are presented and compared on an extensive dataset, comprising a total of 1600 fall trials. Eight machine learning classifiers are implemented for fall-direction classification into four basic directions (forward, backward, right, and left). These are, namely, Bayesian decision making (BDM), least squares method (LSM), k-nearest neighbor classifier (k-NN), artificial neural networks (ANNs), support vector machines (SVMs), decision-tree classifier (DTC), random forest (RF), and adaptive boosting or AdaBoost (AB). BDM achieves perfect classification, followed by k-NN, SVM, and RF. Data acquired from only a single motion sensor unit, worn at the waist of the subject, are processed for experimental verification. Four of the classifiers (BDM, LSM, k-NN, and ANN) are modified to handle the presence of data from an unknown class and evaluated on the same dataset. In this robustness analysis, ANN and k-NN yield accuracies above 96.2%. The results obtained in this study are promising in developing real-world fall-classification systems as they enable fast and reliable classification of fall directions.(c) 2021 Elsevier Inc. All rights reserved.

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

Institutions and Incentives in Knowledge Production and Diffusion: From Science to Innovation

Charles Chadi Ayoubi

EPFL2020