Common genetic effects on risk-taking preferences and choices

Although prior research has shown that risk-taking preferences and choices are correlated across many domains, there is a dearth of research investigating whether these correlations are primarily the result of genetic or environmental factors. We examine the extent to which common genetic factors account for the association between general risk-taking preferences and domain specific risk-taking preferences, and between general risk-taking preferences and risk taking choices in financial investments, stock market participation and business formation. Using data from 1898 monozygotic (MZ) and 1344 same-sex dizygotic (DZ) twins, we find that general risk-taking shares a common genetic component with domain-specific risk-taking preferences and risk-taking choices

Robust correlated and individual component analysis

© 1979-2012 IEEE.Recovering correlated and individual components of two, possibly temporally misaligned, sets of data is a fundamental task in disciplines such as image, vision, and behavior computing, with application to problems such as multi-modal fusion (via correlated components), predictive analysis, and clustering (via the individual ones). Here, we study the extraction of correlated and individual components under real-world conditions, namely i) the presence of gross non-Gaussian noise and ii) temporally misaligned data. In this light, we propose a method for the Robust Correlated and Individual Component Analysis (RCICA) of two sets of data in the presence of gross, sparse errors. We furthermore extend RCICA in order to handle temporal incongruities arising in the data. To this end, two suitable optimization problems are solved. The generality of the proposed methods is demonstrated by applying them onto 4 applications, namely i) heterogeneous face recognition, ii) multi-modal feature fusion for human behavior analysis (i.e., audio-visual prediction of interest and conflict), iii) face clustering, and iv) thetemporal alignment of facial expressions. Experimental results on 2 synthetic and 7 real world datasets indicate the robustness and effectiveness of the proposed methodson these application domains, outperforming other state-of-the-art methods in the field

Correlated-Spaces Regression for Learning Continuous Emotion Dimensions

Adopting continuous dimensional annotations for affective analysis has been gaining rising attention by researchers over the past years. Due to the idiosyncratic nature of this problem, many subproblems have been identified, spanning from the fusion of multiple continuous annotations to exploiting output-correlations amongst emotion dimensions. In this paper, we firstly empirically answer several important questions which have found partial or no answer at all so far in related literature. In more detail, we study the correlation of each emotion dimension (i) with respect to other emotion dimensions, (ii) to basic emotions (e.g., happiness, anger). As a measure for comparison, we use video and audio features. Interestingly enough, we find that (i) each emotion dimension is more correlated with other emotion dimensions rather than with face and audio features, and similarly (ii) that each basic emotion is more correlated with emotion dimensions than with audio and video features. A similar conclusion holds for discrete emotions which are found to be highly correlated to emotion dimensions as compared to audio and/or video features. Motivated by these findings, we present a novel regression algorithm (Correlated-Spaces Regression, CSR), inspired by Canonical Correlation Analysis (CCA) which learns output-correlations and performs supervised dimensionality reduction and multimodal fusion by (i) projecting features extracted from all modalities and labels onto a common space where their inter-correlation is maximised and (ii) learning mappings from the projected feature space onto the projected, uncorrelated label space

Robust correlated and individual component analysis

Recovering correlated and individual components of two, possibly temporally misaligned, sets of data is a fundamental task in disciplines such as image, vision, and behavior computing, with application to problems such as multi-modal fusion (via correlated components), predictive analysis, and clustering (via the individual ones). Here, we study the extraction of correlated and individual components under real-world conditions, namely i) the presence of gross non-Gaussian noise and ii) temporally misaligned data. In this light, we propose a method for the Robust Correlated and Individual Component Analysis (RCICA) of two sets of data in the presence of gross, sparse errors. We furthermore extend RCICA in order to handle temporal incongruities arising in the data. To this end, two suitable optimization problems are solved. The generality of the proposed methods is demonstrated by applying them onto 4 applications, namely i) heterogeneous face recognition, ii) multi-modal feature fusion for human behavior analysis (i.e., audio-visual prediction of interest and conflict), iii) face clustering, and iv) the temporal alignment of facial expressions. Experimental results on 2 synthetic and 7 real world datasets indicate the robustness and effectiveness of the proposed methods on these application domains, outperforming other state-of-the-art methods in the field

The effect of mouth-throat geometry on regional deposition in the tracheobronchial tree

In silico methods offer a valuable approach to predict localized deposition in the tracheobronchial tree, important in the topical treatment of respiratory diseases and the systemic administration of drugs with limited lung bioavailability. In this study, we examine the effect of extrathoracic airway variation on regional deposition in order to assess whether standard mouth-throat models can be adopted for more efficient predictions. Despite large qualitative differences in the extrathoracic airways, deposition patterns and efficiencies in the tracheobronchial region remain largely unaffected for particles smaller than 6 microns. The findings suggest that for drug delivery applications, standard mouth-throat models could be adopted to predict deposition in the central airways

Dynamic probabilistic linear discriminant analysis for video classification

Component Analysis (CA) comprises of statistical techniques that decompose signals into appropriate latent components, relevant to a task-at-hand (e.g., clustering, segmentation, classification). Recently, an explosion of research in CA has been witnessed, with several novel probabilistic models proposed (e.g., Probabilistic Principal CA, Probabilistic Linear Discriminant Analysis (PLDA), Probabilistic Canonical Correlation Analysis). PLDA is a popular generative probabilistic CA method, that incorporates knowledge regarding class-labels and furthermore introduces class-specific and sample-specific latent spaces. While PLDA has been shown to outperform several state-of-the-art methods, it is nevertheless a static model; any feature-level temporal dependencies that arise in the data are ignored. As has been repeatedly shown, appropriate modelling of temporal dynamics is crucial for the analysis of temporal data (e.g., videos). In this light, we propose the first, to the best of our knowledge, probabilistic LDA formulation that models dynamics, the so-called Dynamic-PLDA (DPLDA). DPLDA is a generative model suitable for video classification and is able to jointly model the label information (e.g., face identity, consistent over videos of the same subject), as well as dynamic variations of each individual video. Experiments on video classification tasks such as face and facial expression recognition show the efficacy of the proposed metho

Resonant inelastic x-ray scattering probes the electron-phonon coupling in the spin-liquid kappa-(BEDT-TTF)2Cu2(CN)3

Resonant inelastic x-ray scattering at the N K edge reveals clearly resolved harmonics of the anion plane vibrations in the kappa-(BEDT-TTF)2Cu2(CN)3 spin-liquid insulator. Tuning the incoming light energy at the K edge of two distinct N sites permits to excite different sets of phonon modes. Cyanide CN stretching mode is selected at the edge of the ordered N sites which are more strongly connected to the BEDT-TTF molecules, while positionally disordered N sites show multi-mode excitation. Combining measurements with calculations on an anion plane cluster permits to estimate the sitedependent electron-phonon coupling of the modes related to nitrogen excitation

The value and role of non-invasive prenatal testing in a select South African population

Background. Concerns have been raised about the injudicious use of non-invasive prenatal testing (NIPT) using cell-free DNA (cfDNA), which often leads to inaccuracies in interpretation of the role and value of cfDNA in prenatal screening.Objective. To determine the value and role of NIPT in a select South African (SA) population.Methods. A retrospective review of patients who elected to have NIPT between 1 October 2013 and 30 June 2015 at the Morningside Mediclinic Maternal and Fetal Medicine Centre in Johannesburg, SA. Patients had NIPT after either combined first-trimester screening (CFTS) or a second-trimester ultrasound scan. Data were collected on details of the first- and/or second-trimester screening, results of the NIPT, invasive tests done, decisions made in the event of abnormal results, and pregnancy outcomes.Results. Overall, 3 473 first- and second-trimester fetal assessments were done at the centre during the study period, and 2.3% of patients (n=82) elected to have NIPT. The majority of these individuals elected to have NIPT on the basis of positive findings on CFTS, or markers of aneuploidy detected on a second-trimester ultrasound scan. Of the tests done, 97.6% produced results. Of those with no results, one did not meet quality metrics and the other had a low fetal fraction of cfDNA. There were two abnormal NIPT results, one indicating a high risk of trisomy 13 and the other a triploidy. Patients who screened negative elected not to have an invasive test.Conclusion. The value of NIPT in this study was that it made it possible to avoid a number of invasive tests. NIPT had a role in contingency screening

Facial affect "in the wild": a survey and a new database

Well-established databases and benchmarks have been developed in the past 20 years for automatic facial behaviour analysis. Nevertheless, for some important problems regarding analysis of facial behaviour, such as (a) estimation of affect in a continuous dimensional space (e.g., valence and arousal) in videos displaying spontaneous facial behaviour and (b) detection of the activated facial muscles (i.e., facial action unit detection), to the best of our knowledge, well-established in-the-wild databases and benchmarks do not exist. That is, the majority of the publicly available corpora for the above tasks contain samples that have been captured in controlled recording conditions and/or captured under a very specific milieu. Arguably, in order to make further progress in automatic understanding of facial behaviour, datasets that have been captured in in the-wild and in various milieus have to be developed. In this paper, we survey the progress that has been recently made on understanding facial behaviour in-the-wild, the datasets that have been developed so far and the methodologies that have been developed, paying particular attention to deep learning techniques for the task. Finally, we make a significant step further and propose a new comprehensive benchmark for training methodologies, as well as assessing the performance of facial affect/behaviour analysis/ understanding in-the-wild. To the best of our knowledge, this is the first time that such a benchmark for valence and arousal "in-the-wild" is presente