Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109

Altered synaptic plasticity and behavioral abnormalities in CNGA3-deficient mice

The role of the cyclic nucleotide-gated (CNG) channel CNGA3 is well established in cone photoreceptors and guanylyl cyclase-D-expressing olfactory neurons. To assess a potential function of CNGA3 in the mouse amygdala and hippocampus, we examined synaptic plasticity and performed a comparative analysis of spatial learning, fear conditioning and step-down avoidance in wild-type mice and CNGA3 null mutants (CNGA3(-/-) ). CNGA3(-/-) mice showed normal basal synaptic transmission in the amygdala and the hippocampus. However, cornu Ammonis (CA1) hippocampal long-term potentiation (LTP) induced by a strong tetanus was significantly enhanced in CNGA3(-/-) mice as compared with their wild-type littermates. Unlike in the hippocampus, LTP was not significantly altered in the amygdala of CNGA3(-/-) mice. Enhanced hippocampal LTP did not coincide with changes in hippocampus-dependent learning, as both wild-type and mutant mice showed a similar performance in water maze tasks and contextual fear conditioning, except for a trend toward higher step-down latencies in a passive avoidance task. In contrast, CNGA3(-/-) mice showed markedly reduced freezing to the conditioned tone in the amygdala-dependent cued fear conditioning task. In conclusion, our study adds a new entry on the list of physiological functions of the CNGA3 channel. Despite the dissociation between physiological and behavioral parameters, our data describe a so far unrecognized role of CNGA3 in modulation of hippocampal plasticity and amygdala-dependent fear memory

Chemo- and Thermosensory Responsiveness of Grueneberg Ganglion Neurons Relies on Cyclic Guanosine Monophosphate Signaling Elements

Neurons of the Grueneberg ganglion (GG) in the anterior nasal region of mouse pups respond to cool temperatures and to a small set of odorants. While the thermosensory reactivity appears to be mediated by elements of a cyclic guanosine monophosphate (cGMP) cascade, the molecular mechanisms underlying the odor-induced responses are unclear. Since odor-responsive GG cells are endowed with elements of a cGMP pathway, specifically the transmembrane guanylyl cyclase subtype GC-G and the cyclic nucleotide-gated ion channel CNGA3, the possibility was explored whether these cGMP signaling elements may also be involved in chemosensory GG responses. Experiments with transgenic mice deficient for GC-G or CNGA3 revealed that GG responsiveness to given odorants was significantly diminished in these knockout animals. These findings suggest that a cGMP cascade may be important for both olfactory and thermosensory signaling in the GG. However, in contrast to the thermosensory reactivity, which did not decline over time, the chemosensory response underwent adaptation upon extended stimulation, suggesting that the two transduction processes only partially overlap. Copyright (C) 2011 S. Karger AG, Base

Social Preferences, Skill Segregation and Wage Dynamics

We study the earning structure and the equilibrium asignment of workers to firms in a model in which workers have social preferences, and skills are perfectly substitutable in production. Firms offer long-term contracts, and we allow for frictions in the labour market in the form of mobility costs. The model delivers specific predictions about the nature of worker flows, about the characteristic of workplace skill segregation, and about wage dispersion both within and cross firms. We shows that long-term contracts in the resence of social preferences associate within-firm wage dispersion with novel "internal labour market" features such as gradual promotions, productivity-unrelated wage increases, and downward wage flexibility. These three dynamic features lead to productivity-unrelated wage volatily within firms.Publicad

Genuine Personality Recognition from Highly Constrained Face Images

People are able to accurately estimate personality traits, merely on the basis of \u201cpassport\u201d-style neutral faces and, thus, cues must exist that allow for such estimation. However, up to date, there has been little progress in identifying the form and location of these cues. In this paper we address the problem of inferring true personality traits in highly constrained images using state of art machine learning techniques, in particular, deep networks and class activation maps analysis. The novelty of our work consists in that, differently from the vast majority of the current and past approaches (that refer to the problem of consensus personality rating prediction) we predict the genuine personality based on highly constrained images: the target\u2019s are self ratings on a validated personality inventory and we restrict to passport-like photos, in which so-called controllable cues are minimized. Our results show that self-reported personality traits can be accurately evaluated from facial features. A preliminar analysis on the features activation maps shows promising results for a deeper understanding on relevant facial cues for traits estimation

O(N) methods in electronic structure calculations

Linear scaling methods, or O(N) methods, have computational and memory requirements which scale linearly with the number of atoms in the system, N, in contrast to standard approaches which scale with the cube of the number of atoms. These methods, which rely on the short-ranged nature of electronic structure, will allow accurate, ab initio simulations of systems of unprecedented size. The theory behind the locality of electronic structure is described and related to physical properties of systems to be modelled, along with a survey of recent developments in real-space methods which are important for efficient use of high performance computers. The linear scaling methods proposed to date can be divided into seven different areas, and the applicability, efficiency and advantages of the methods proposed in these areas is then discussed. The applications of linear scaling methods, as well as the implementations available as computer programs, are considered. Finally, the prospects for and the challenges facing linear scaling methods are discussed.Comment: 85 pages, 15 figures, 488 references. Resubmitted to Rep. Prog. Phys (small changes