Recent state-of-the-art algorithms in photometric stereo rely on neural networks and operate either through prior learning or inverse rendering optimization. Here, we revisit the problem of calibrated photometric stereo by leveraging recent advances in 3D inverse rendering using the Gaussian Splatting formalism. This allows us to parameterize the 3D scene to be reconstructed and optimize it in a more interpretable manner. Our approach incorporates a simplified model for light representation and demonstrates the potential of the Gaussian Splatting rendering engine for the photometric stereo problem.

Linear and Quadratic Discriminant Analysis (LDA and QDA) are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust. This paper studies the robustness to scale changes in the data of a new discriminant analysis technique where each data point is drawn by its own arbitrary Elliptically Symmetrical (ES) distribution and its own arbitrary scale parameter. Such a model allows for possibly very heterogeneous, independent but non-identically distributed samples. The new decision rule derived is simple, fast and robust to scale changes in the data compared to others state-of-the-art methods.

Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing maximum likelihood estimate when dealing with Gaussian Mixture Model (GMM). When the sample size is smaller than the data dimension, this could lead to a singular or poorly conditioned covariance matrix and, thus, to performance reduction. This paper presents a regularized version of the EM algorithm that efficiently uses prior knowledge to cope with a small sample size. This method aims to maximize a penalized GMM likelihood where regularized estimation may ensure positive definiteness of covariance matrix updates by shrinking the estimators towards some structured target covariance matrices. Finally, experiments on real data highlight the good performance of the proposed algorithm for clustering purposes.

Personality is considered one of the most influential research topics in psychology, as it predicts many consequential outcomes such as mental and physical health and explains human behaviour. With the widespread use of social networks as a means of communication, it is becoming increasingly important to develop models that can automatically and accurately read the essence of individuals based solely on their writing. In particular, the convergence of social and computer sciences has led researchers to develop automatic approaches for extracting and studying "hidden" information in textual data on the internet. The nature of this thesis project is highly experimental, and the motivation behind this work is to present detailed analyses on the topic, as currently there are no significant investigations of this kind. The objective is to identify an adequate semantic space that allows for defining the personality of the object to which a certain text refers. The starting point is a dictionary of adjectives that psychological literature defines as markers of the five major personality traits, or Big Five. In this work, we started with the implementation of fully-connected neural networks as a basis for understanding how simple deep learning models can provide information on hidden personality characteristics. Finally, we use a class of distributional algorithms invented in 2013 by Tomas Mikolov, which consists of using a convolutional neural network that learns the contexts of words in an unsupervised way. In this way, we construct an embedding that contains the semantic information on the text, obtaining a kind of "geometry of meaning" in which concepts are translated into linear relationships. With this last experiment, we hypothesize that an individual writing style is largely coupled with their personality traits.

Launched last November 19, the 5-year LaborIA program, financed by the Ministry of Labor, Employment and Integration and operated by Matrice, an institute for technological and social innovation, has been joined by Inria. Its mission is to " better understand artificial intelligence and its effects on work, employment, skills and social dialogue in order to change business practices and public action . In September, Matrice will begin a survey to better measure the impact of AI in 250 companies as well as field investigations. LaborIA is part of the PMIA initiative, which aims to bridge the gap between AI theory and practice by supporting cutting-edge research and applied activities on AI-related priorities. One of the working groups of this initiative is dedicated to the "Future of Work" theme, it is affiliated with the PMIA Expertise Center in Paris and hosted by INRIA and conducts, among other things, analyses on how AI affects and will affect workers and their environment. According to the OECD, 32% of jobs will be impacted by automation over the next twenty years. "The transformations that our society is undergoing, such as the digital and ecological transitions, have an impact that can be observed concretely in our daily lives.

The following document pursues the objective of comparing some useful methods to balance a dataset and obtain a trained model. The dataset used for training is made up of short and medium length sentences, such as simple phrases or extracts from conversations that took place on web channels. The training of the models will take place with the help of the structures made available by the Apache Spark framework, the models may subsequently be useful for a possible implementation of a solution capable of classifying sentences using the distributed environment, as described in "New frontier of textual classification: Big data and distributed calculation" by Massimiliano Morrelli et al.

We propose a MAP Bayesian approach to perform and evaluate a co-clustering of mixed-type data tables. The proposed model infers an optimal segmentation of all variables then performs a co-clustering by minimizing a Bayesian model selection cost function. One advantage of this approach is that it is user parameter-free. Another main advantage is the proposed criterion which gives an exact measure of the model quality, measured by probability of fitting it to the data. Continuous optimization of this criterion ensures finding better and better models while avoiding data over-fitting. The experiments conducted on real data show the interest of this co-clustering approach in exploratory data analysis of large data sets.