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Concept

Recherche empirique

La recherche empirique explore le monde sensible en s'appuyant sur l’expérimentation, l’observation, ainsi que sur un processus d'évaluation par les pairs qui permet de faire un tri dans les connaissances scientifiques produites et de ne garder, au fil du temps, que ce qui reste valide. Il existe deux types de sciences empiriques : les sciences humaines et sociales ainsi que les sciences naturelles. Cycle du raisonnement Le raisonnement empirique se déroule en différentes étapes qui se répètent, c'est pourquoi on parle même de cycle de raisonnement.

l'observation d'un phénomène, permettant au scientifique de collecter des données et des informations pour préparer les hypothèses qu'il va émettre.

#l'induction, où le scientifique va du coup émettre ses hypothèses.

déduction, autrement dit le scientifique déduit les conséquences des hypothèses qu'il a posées.

#phase de test, où le scientifique va vérifier ses hypothèses. #l'évaluation, celle où le scientifique va pouvo

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FIN-407: Financial econometrics

This course aims to give an introduction to the application of machine learning to finance. These techniques gained popularity due to the limitations of traditional financial econometrics methods tackling big data. We will review and compare traditional methods and machine learning algorithms.

CS-411: Digital education

This course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies on this topic: do student actually learn due to technologies?

FIN-405: Investments

The course covers a wide range of topics in investment analysis

Méthode scientifique

La méthode scientifique désigne l'ensemble des canons guidant ou devant guider le processus de production des connaissances scientifiques, qu'il s'agisse d'observations, d'expériences, de raisonnement

Sociologie

La sociologie est une discipline des sciences sociales qui a pour objectif de rechercher des explications et des compréhensions typiquement sociales, et non pas mentales ou biophysiques, à des phéno

Psychologie

La psychologie (du grec , « âme », et , « parole, discours »), est une discipline scientifique qui s'intéresse à l'étude du corpus des connaissances sur les faits psychiques, les comportements et le

Nothing New Under the Sun: Novelty Constructs and Measures in Social Studies

Davide Bavato

The concept of novelty is central to questions of creativity, innovation, and discovery. Despite the prominence in scientific inquiry and everyday discourse, there is a chronic ambiguity over its meaning and a surprising variety of empirical measures, which muddle the interpretation of prior findings and frustrate the consolidation of knowledge. To help dispel some of the unclarity, this paper presents a survey and synthesis of conceptualizations and operationalizations of novelty scattered across social, cognitive, and organizational studies. From this analysis, I advance the argument that novelty is generally regarded as a function of frequency or proximity, and in these two complementary perspectives, it is commonly bounded its empirical study and theoretical understanding. I further argue that contextual and temporal aspects are integral to the specification of novelty and primary contributors to its multifaceted nature.

Emerald Publishing Limited2022

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Stochastic Zeroth-Order Optimisation Algorithms with Variance Reduction

Ahmad Ajalloeian

Introduction of optimisation problems in which the objective function is black box or obtaining the gradient is infeasible, has recently raised interest in zeroth-order optimisation methods. As an example finding adversarial examples for Deep Learning models (Chen et al. (2017); Moosavi-Dezfooli et al. (2016)) is one of the most common applications in which zeroth-order methods could be used. These optimisation methods use only function values at certain points to estimate the gradient. Most current approaches iteratively sample a random search direction along which they compute an estimation of the gradient (Nesterov and Spokoiny (2017); Conn et al. (2009); Wibisono et al. (2012)). However, due to the high variance in the search direction, these methods usually need d times more iterations than the standard gradient methods, where d is the dimensionality of the problem. So it seems that the main effort for improving the zeroth-order methods should be in reducing the variance of the gradient estimate. In this work we will analyse the gradient-free oracle which uses random directions sampled form a Gaussian distribution. Our analysis shows that in smooth and strongly convex setting, we have a convergence rate of O( d/T) which clearly shows the dependency to the dimension of the problem. Furthermore we propose some variance reduction methods to make the zeroth-order optimisation faster. We experiment our proposed methods in Python to compare their convergence in stochastic and non-stochastic setting. Our empirical results show that in a setting that number of allowed function evaluation is fixed, using a variance reduction method (e.g. momentum) can make the convergence of zeroth-order methods happen faster.

2019

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A physics-based model for wind turbine wake expansion in the atmospheric boundary layer

Fernando Porté Agel, Dara Vahidi

Analytical wind turbine wake models are widely used to predict the wake velocity deficit. In these models, the wake growth rate is a key parameter specified mainly with empirical formulations. In this study, a new physics-based model is proposed and validated to predict the wake expansion downstream of a turbine based on the incoming ambient turbulence and turbine operating conditions. The new model utilises Taylor diffusion theory, the Gaussian wake model, turbulent mixing layer theory and the analogy between wind turbine wake expansion and scalar diffusion. These components ensure that the model conserves mass and momentum in the far wake and accounts for the ambient turbulence and turbine-induced turbulence effects on the wake expansion. To account for the turbulence relevant scales that contribute to the wake expansion, the model uses the root-mean-square of the low-pass filtered radial velocity component. A simplified version that only requires the unfiltered velocity standard deviation and turbulence integral scale is also proposed. In addition, a new relation for the near-wake length is derived. The model performance is validated using large-eddy simulation data of a wind turbine wake under neutral atmospheric conditions with a wide range of incoming turbulence levels. The results show that the proposed model yields reasonable predictions of the wake width, maximum velocity deficit and near-wake length. In the case with a relatively low incoming streamwise turbulence intensity of 0.05, the ambient and turbine-induced terms in the model contribute almost equally to the wake width, rendering them both crucial for reasonable wake predictions.

2022

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