The Many Moods of Emotion

This paper presents a novel approach to the facial expression generation problem. Building upon the assumption of the psychological community that emotion is intrinsically continuous, we first design our own continuous emotion representation with a 3-dimensional latent space issued from a neural network trained on discrete emotion classification. The so-obtained representation can be used to annotate large in the wild datasets and later used to trained a Generative Adversarial Network. We first show that our model is able to map back to discrete emotion classes with a objectively and subjectively better quality of the images than usual discrete approaches. But also that we are able to pave the larger space of possible facial expressions, generating the many moods of emotion. Moreover, two axis in this space may be found to generate similar expression changes as in traditional continuous representations such as arousal-valence. Finally we show from visual interpretation, that the third remaining dimension is highly related to the well-known dominance dimension from psychology

CAKE: Compact and Accurate K-dimensional representation of Emotion

Numerous models describing the human emotional states have been built by the psychology community. Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are becoming interesting features extraction tools in many computer vision tasks.Inspired by works from the psychology community, we first study the link between the compact two-dimensional representation of the emotion known as arousal-valence, and discrete emotion classes (e.g. anger, happiness, sadness, etc.) used in the computer vision community. It enables to assess the benefits -- in terms of discrete emotion inference -- of adding an extra dimension to arousal-valence (usually named dominance). Building on these observations, we propose CAKE, a 3-dimensional representation of emotion learned in a multi-domain fashion, achieving accurate emotion recognition on several public datasets. Moreover, we visualize how emotions boundaries are organized inside DNN representations and show that DNNs are implicitly learning arousal-valence-like descriptions of emotions. Finally, we use the CAKE representation to compare the quality of the annotations of different public datasets

Evolution of the fine-structure constant in runaway dilaton models

We study the detailed evolution of the fine-structure constant $\alpha$ in the string-inspired runaway dilaton class of models of Damour, Piazza and Veneziano. We provide constraints on this scenario using the most recent $\alpha$ measurements and discuss ways to distinguish it from alternative models for varying $\alpha$. For model parameters which saturate bounds from current observations, the redshift drift signal can differ considerably from that of the canonical $\Lambda$CDM paradigm at high redshifts. Measurements of this signal by the forthcoming European Extremely Large Telescope (E-ELT), together with more sensitive $\alpha$ measurements, will thus dramatically constrain these scenarios.Comment: 11 pages, 4 figure

Probing dark energy beyond z=2z=2 with CODEX

Precision measurements of nature's fundamental couplings and a first measurement of the cosmological redshift drift are two of the key targets for future high-resolution ultra-stable spectrographs such as CODEX. Being able to do both gives CODEX a unique advantage, allowing it to probe dynamical dark energy models (by measuring the behavior of their equation of state) deep in the matter era and thereby testing classes of models that would otherwise be difficult to distinguish from the standard $\Lambda$CDM paradigm. We illustrate this point with two simple case studies.Comment: 4 pages, 4 figures; submitted to Phys. Rev.

Titanium in phengite: a geobarometer for high temperature eclogites

Phengite chemistry has been investigated in experiments on a natural SiO2-TiO2-saturated greywacke and a natural SiO2-TiO2-Al2SiO5-saturated pelite, at 1.5-8.0GPa and 800-1,050°C. High Ti-contents (0.3-3.7 wt %), Ti-enrichment with temperature, and a strong inverse correlation of Ti-content with pressure are the important features of both experimental series. The changes in composition with pressure result from the Tschermak substitution (Si+R2+=AlIV+AlVI) coupled with the substitution: AlVI+Si=Ti+AlIV. The latter exchange is best described using the end-member Ti-phengite (KMgTi[Si3Al]O10(OH)2, TiP). In the rutile-quartz/coesite saturated experiments, the aluminoceladonite component increases with pressure while the muscovite, paragonite and Ti-phengite components decrease. A thermodynamic model combining data obtained in this and previous experimental studies are derived to use the equilibrium MgCel+Rt=TiP+Cs/Qz as a thermobarometer in felsic and basic rocks. Phengite, rutile and quartz/coesite are common phases in HT-(U)HP metamorphic rocks, and are often preserved from regression by entrapment in zircon or garnet, thus providing an opportunity to determine the T-P conditions of crystallization of these rocks. Two applications on natural examples (Sulu belt and Kokchetav massif) are presented and discussed. This study demonstrates that Ti is a significant constituent of phengites that could have significant effects on phase relationships and melting rates with decreasing P or increasing T in the continental crus

Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning

This paper proposes a step toward obtaining general models of knowledge for facial analysis, by addressing the question of multi-source transfer learning. More precisely, the proposed approach consists in two successive training steps: the first one consists in applying a combination operator to define a common embedding for the multiple sources materialized by different existing trained models. The proposed operator relies on an auto-encoder, trained on a large dataset, efficient both in terms of compression ratio and transfer learning performance. In a second step we exploit a distillation approach to obtain a lightweight student model mimicking the collection of the fused existing models. This model outperforms its teacher on novel tasks, achieving results on par with state-of-the-art methods on 15 facial analysis tasks (and domains), at an affordable training cost. Moreover, this student has 75 times less parameters than the original teacher and can be applied to a variety of novel face-related tasks