Human-controllable and structured deep generative models

Deep generative models are a class of probabilistic models that attempts to learn the underlying data distribution. These models are usually trained in an unsupervised way and thus, do not require any labels. Generative models such as Variational Autoencoders and Generative Adversarial Networks have made astounding progress over the last years. These models have several benefits: eased sampling and evaluation, efficient learning of low-dimensional representations for downstream tasks, and better understanding through interpretable representations. However, even though the quality of these models has improved immensely, the ability to control their style and structure is limited. Structured and human-controllable representations of generative models are essential for human-machine interaction and other applications, including fairness, creativity, and entertainment. This thesis investigates learning human-controllable and structured representations with deep generative models. In particular, we focus on generative modelling of 2D images. For the first part, we focus on learning clustered representations. We propose semi-parametric hierarchical variational autoencoders to estimate the intensity of facial action units. The semi-parametric model forms a hybrid generative-discriminative model and leverages both parametric Variational Autoencoder and non-parametric Gaussian Process autoencoder. We show superior performance in comparison with existing facial action unit estimation approaches. Based on the results and analysis of the learned representation, we focus on learning Mixture-of-Gaussians representations in an autoencoding framework. We deviate from the conventional autoencoding framework and consider a regularized objective with the Cauchy-Schwarz divergence. The Cauchy-Schwarz divergence allows a closed-form solution for Mixture-of-Gaussian distributions and, thus, efficiently optimizing the autoencoding objective. We show that our model outperforms existing Variational Autoencoders in density estimation, clustering, and semi-supervised facial action detection. We focus on learning disentangled representations for conditional generation and fair facial attribute classification for the second part. Conditional image generation relies on the accessibility to large-scale annotated datasets. Nevertheless, the geometry of visual objects, such as in faces, cannot be learned implicitly and deteriorate image fidelity. We propose incorporating facial landmarks with a statistical shape model and a differentiable piecewise affine transformation to separate the representation for appearance and shape. The goal of incorporating facial landmarks is that generation is controlled and can separate different appearances and geometries. In our last work, we use weak supervision for disentangling groups of variations. Works on learning disentangled representation have been done in an unsupervised fashion. However, recent works have shown that learning disentangled representations is not identifiable without any inductive biases. Since then, there has been a shift towards weakly-supervised disentanglement learning. We investigate using regularization based on the Kullback-Leiber divergence to disentangle groups of variations. The goal is to have consistent and separated subspaces for different groups, e.g., for content-style learning. Our evaluation shows increased disentanglement abilities and competitive performance for image clustering and fair facial attribute classification with weak supervision compared to supervised and semi-supervised approaches.Open Acces

La relation entre flux d entrées nets et performance des fonds : une étude appliquée au cas des OPCVM actions français

Cet article étudie la relation entre les flux nets et la rentabilité relative des fonds actions français pendant la période 1992-2007. En utilisant la méthode des doubles clusters, on montre qu'il existe une relation convexe entre les flux nets et le rang de performance pour l'année précédente. Ainsi, au sein des fonds « stars » le rang de performance influence positivement l'attractivité du fonds, alors que pour les fonds de performance relative moyenne ou faible, il n'y a pas d'effet des performances passées. Nous montrons aussi que les calculs de rentabilité se fondent vraisemblablement sur des horizons courts. De plus comme dans le cas américain, la convexité est plus importante pour les « jeunes » fonds français. Toutefois, la relation est quantitativement et qualitativement moins marquée que dans le cas américain, ce qui traduit probablement les spécificités françaises en matière de distribution des fonds. Enfin, la convexité n'apparaît que dans les segments les moins spécialisés du marché (France et Europe) ce qui pourrait traduire le faible degré de sophistication de leur clientèle d'investisseurs.Fonds de placement

DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding

Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation. Yet, most existing VAE-based methods apply classifiers learned separately from the encoded features. By contrast, the non-parametric (probabilistic) approaches, such as Gaussian Processes (GPs), typically outperform their parametric counterparts, but cannot deal easily with large amounts of data. To this end, we propose a novel VAE semi-parametric modeling framework, named DeepCoder, which combines the modeling power of parametric (convolutional) and nonparametric (ordinal GPs) VAEs, for joint learning of (1) latent representations at multiple levels in a task hierarchy1, and (2) classification of multiple ordinal outputs. We show on benchmark datasets for AU intensity estimation that the proposed DeepCoder outperforms the state-of-the-art approaches, and related VAEs and deep learning models.Comment: ICCV 2017 - accepte

La prévention des déchets : une analyse empirique des déterminants du comportement des entreprises

Depuis 2008, avec la politique européenne des déchets et le Grenelle de l’Environnement, les entreprises sont incitées à adopter des stratégies préventives de gestion des déchets en les réduisant à la source. L’objet de cet article est d’analyser les déterminants d’une stratégie préventive. Nous avons utilisé les données issues d’une enquête menée par un des auteurs sur les stratégies de gestion et de prévention des déchets solides et non dangereux, auprès de 404 PME françaises en 2014. Les résultats montrent que les pressions et les collaborations avec les parties prenantes (clients, fournisseurs, prestataires) jouent un rôle important dans la décision d’adopter la prévention. De même, le fait de posséder des accréditations environnementales et d’autres caractéristiques propres à l’entreprise influencent significativement le choix en faveur d’une stratégie préventive. En revanche, les pressions réglementaires ne constituent pas un facteur déclencheur poussant l’entreprise à adopter la prévention des déchets.Since 2008, with the European waste policy and France’s Grenelle Environment Round Table, firms have been encouraged to adopt waste prevention strategies. The aim of this article is to analyze the factors that determine companies’ implementation of these strategies. We use data from a survey conducted by one of the authors on management and prevention strategies of solid and non-hazardous waste for 404 French SMEs in 2014. The results show that the pressure and relationships with stakeholders (clients, suppliers and service providers) play an important role in companies’ decisions to adopt prevention strategies. In addition, having environmental accreditations and other company-specific characteristics significantly influence the implementation of waste-prevention strategies. In contrast, regulation pressures do not seem to drive companies to adopt waste prevention strategies

The local uniform convergence of positive harmonic function sequence

The Harnack distance on space  and its conformal invariance were constructed and studied by Herron. In this paper, we obtain the Harnack distance on domains  in . Then, we use this concept to investigate some properties of the positive harmonic function class. These results are obtained in the complex plane, so it is advantageous to take some tools of the complex analysis. The main result of this paper is the property of the local uniform convergence of the positive harmonic sequences on a domain in the complex plane.The Harnack distance on space  and its conformal invariance were constructed and studied by Herron. In this paper, we obtain the Harnack distance on domains  in . Then, we use this concept to investigate some properties of the positive harmonic function class. These results are obtained in the complex plane, so it is advantageous to take some tools of the complex analysis. The main result of this paper is the property of the local uniform convergence of the positive harmonic sequences on a domain in the complex plane

Electrical Power Exchange in GMS and Its Influence on Power Systems in Vietnam and Thailand

The paper aims to identify the development of power interconnection network in the Greater Mekong Sub-region (GMS) which is a part of the major energy infrastructure mandated by ASEAN delegates in 1997. An overview of power systems in the region is introduced. The combined load curve for Vietnam and Thailand are formed to show the benefit of power grid interconnection of GMS. The paper also concentrates on simulation, analysis and evaluation of power transfer in 500kV and 220kV interconnection transmission lines in GMS for the planning horizon of 2010-2020. Reliability and environmental benefits of the interconnection are discussed due to interconnection. Based on the simulation results few recommendations are given

ASSESSING THE POTENTIAL OF COMMUNITY-BASED ECOTOURISM TOWARD SUSTAINABLE DEVELOPMENT: A CASE STUDY IN TUA CHUA KARST PLATEAU – DIEN BIEN – VIET NAM

Tua Chua Karst Plateau - a living area of the Mong ethnic group with spectacular natural landscapes, cool weather, and unique indigenous cultural values. They are all prerequisites for tourism development. This study aims to evaluate the potential of community-based ecotourism development in the Tua Chua Karst Plateau. This research uses the AHP method. Evaluation criteria include (i) uniqueness of natural landscape, (ii) indigenous cultural value, (iii) stakeholder engagement, (iv) local tourism development policy, (v) quality of infrastructure, (vi) quality of tourism facilities, (vii) accessibility, (viii) connectivity. The evaluation system includes eight criteria that have classified tourism resources according to each resource point and identified suitable internal and external potentials to exploit the geological value of the plateau and preserve indigenous culture. Results of this study reveal that the indigenous cultural values, the participation of local communities, and the uniqueness of the natural landscape have an important impact on the development of ecotourism. Tua Chua Karst Plateau has great potential for community-based ecotourism development with 14 tourist resource sites, of which 8 are highly appreciated

Antibiotic Resistance Profile and Methicillin-Resistant Encoding Genes of Staphylococcus aureus Strains Isolated from Bloodstream Infection Patients in Northern Vietnam

Background:  Evaluating the antibiotic susceptibility and resistance genes is essential in the clinical management of bloodstream infections (BSIs). Nevertheless, there are still limited studies in Northern Vietnam. AIM: This study aimed to determine the antibiotic resistance profile and methicillin-resistant encoding genes of Staphylococcus aureus (S. aureus) causing BSIs in Northern Vietnam. METHODS: The cross-sectional study was done from December 2012 to June 2014 in two tertiary hospitals in Northern Vietnam. Tests performed at the lab of the hospital. RESULTS:  In 43 S. aureus strains isolating, 53.5 % were MRSA. Distribution of gene for overall, MRSA, and MSSA strains were following: mecA gene (58.1 %; 95.7%, and 15%), femA gene (48.8%, 47.8%, and 50%), femB gene (88.4%, 82.6%, and 95%). Antibiotic resistance was highest in penicillin (100%), followed by erythromycin (65.1%) and clindamycin (60.5%). Several antibiotics were susceptible (100%), including vancomycin, tigecycline, linezolid, quinupristin/dalfopristin. Quinolone group was highly sensitive, include ciprofloxacin (83.7%), levofloxacin (86%) and moxifloxacin (86%). CONCLUSION:  In S. aureus causing BSIs, antibiotic resistance was higher in penicillin, erythromycin, and clindamycin. All strains were utterly susceptible to vancomycin, tigecycline, linezolid, quinupristin/dalfopristin

Antibiotic Resistance Profile and Diversity of Subtypes Genes in Escherichia coli Causing Bloodstream Infection in Northern Vietnam

BACKGROUND: Evaluating the antibiotic susceptibility and resistance genes is essential in the clinical management of bloodstream infections (BSIs). But there are still limited studies in Northern Vietnam. AIM: The aim of the study was to determine the antibiotic resistance profile and characteristics of subtypes genes in Escherichia coli causing BSIs in Northern Vietnam. METHODS: The cross-sectional study was done in the period from December 2012 to June 2014 in two tertiary hospitals in Northern Vietnam. Tests were performed at the lab of the hospital. RESULTS: In 56 E. coli strains isolating 39.29 % produced ESBL. 100% of the isolates harbored blaTEM gene, but none of them had the blaPER gene. The prevalence of ESBL producers and ESBL non-producers in blaCTX-M gene was 81.82%, and 73.53%, in blaSHV gene was 18.18% and 35.29%. Sequencing results showed three blaTEM subtypes (blaTEM 1, 79, 82), four blaCTX-M subtypes (blaCTX-M-15, 73, 98, 161), and eight blaSHV subtypes (blaSHV 5, 7, 12, 15, 24, 33, 57, 77). Antibiotic resistance was higher in ampicillin (85.71%), trimethoprim/sulfamethoxazole (64.29%) and cephazolin (50%). Antibiotics were still highly susceptible including doripenem (96.43%), ertapenem (94.64%), amikacin (96.43%), and cefepime (89.29%). CONCLUSION: In Escherichia coli causing BSIs, antibiotic resistance was higher in ampicillin, trimethoprim/sulfamethoxazole and cephazolin. Antibiotics was highly susceptible including doripenem, ertapenem, amikacin, and cefepime

Depression, anxiety and stress among healthcare workers in the context of the COVID-19 pandemic: a cross-sectional study in a tertiary hospital in Northern Vietnam

IntroductionThe outbreak of coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) had significant effects on the mental well-being in general, particularly for healthcare professionals. This study examined the prevalence of depression, anxiety, and stress, and identified the associated risk factors amongst healthcare workers during the COVID-19 outbreak in a tertiary hospital located in Vietnam.MethodsWe conducted a cross-sectional study at a tertiary-level hospital, where the Depression Anxiety and Stress Scale 21 (DASS-21) web-based questionnaire was employed. We analyzed the determinant factors by employing multivariate logistic models.ResultsThe prevalence of depression, anxiety, and stress symptoms were 19.2%, 24.7%, and 13.9%, respectively. Factors such as engaging in shift work during the pandemic, taking care of patients with COVID-19, and staff’s health status were associated with mental health issues among health professionals. In addition, having alternate rest periods was likely to reduce the risk of stress.ConclusionThe prevalence of mental health problems in healthcare workers during the COVID-19 pandemic was relatively high. Having resting periods could potentially mitigate the development of stress among health professionals. Our findings could be taken into account for improving mental health of the health professional population