Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality

In the modern era, each Internet user leaves enormous amounts of auxiliary digital residuals (footprints) by using a variety of on-line services. All this data is already collected and stored for many years. In recent works, it was demonstrated that it's possible to apply simple machine learning methods to analyze collected digital footprints and to create psycho-demographic profiles of individuals. However, while these works clearly demonstrated the applicability of machine learning methods for such an analysis, created simple prediction models still lacks accuracy necessary to be successfully applied for practical needs. We have assumed that using advanced deep machine learning methods may considerably increase the accuracy of predictions. We started with simple machine learning methods to estimate basic prediction performance and moved further by applying advanced methods based on shallow and deep neural networks. Then we compared prediction power of studied models and made conclusions about its performance. Finally, we made hypotheses how prediction accuracy can be further improved. As result of this work, we provide full source code used in the experiments for all interested researchers and practitioners in corresponding GitHub repository. We believe that applying deep machine learning for psycho-demographic profiling may have an enormous impact on the society (for good or worse) and provides means for Artificial Intelligence (AI) systems to better understand humans by creating their psychological profiles. Thus AI agents may achieve the human-like ability to participate in conversation (communication) flow by anticipating human opponents' reactions, expectations, and behavior

Impacts of Fire Emissions and Transport Pathways on the Interannual Variation of CO In the Tropical Upper Troposphere

This study investigates the impacts of fire emission, convection, various climate conditions and transport pathways on the interannual variation of carbon monoxide (CO) in the tropical upper troposphere (UT), by evaluating the field correlation between these fields using multi-satellite observations and principle component analysis, and the transport pathway auto-identification method developed in our previous study. The rotated empirical orthogonal function (REOF) and singular value decomposition (SVD) methods are used to identify the dominant modes of CO interannual variation in the tropical UT and to study the coupled relationship between UT CO and its governing factors. Both REOF and SVD results confirm that Indonesia is the most significant land region that affects the interannual variation of CO in the tropical UT, and El Nino-Southern Oscillation (ENSO) is the dominant climate condition that affects the relationships between surface CO emission, convection and UT CO. In addition, our results also show that the impact of El Nino on the anomalous CO pattern in the tropical UT varies strongly, primarily due to different anomalous emission and convection patterns associated with different El Nino events. In contrast, the anomalous CO pattern in the tropical UT during La Nina period appears to be less variable among different events. Transport pathway analysis suggests that the average CO transported by the "local convection" pathway (Delta COlocal) accounts for the differences of UT CO between different ENSO phases over the tropical continents during biomass burning season. Delta COlocal is generally higher over Indonesia-Australia and lower over South America during El Nino years than during La Nina years. The other pathway ("advection within the lower troposphere followed by convective vertical transport") occurs more frequently over the west-central Pacific during El Nino years than during La Nina years, which may account for the UT CO differences over this region between different ENSO phases.NASA Aura Science Team (AST) program NNX09AD85GJackson School of Geosciences at the University of Texas at AustinJet Propulsion Laboratory, California Institute of Technology, under NASAGeological Science

The Magnetic Topology of the Weak-Lined T Tauri Star V410 - A Simultaneous Temperature and Magnetic Field Inversion

We present a detailed temperature and magnetic investigation of the T Tauri star V410 Tau by means of a simultaneous Doppler- and Zeeman-Doppler Imaging. Moreover we introduce a new line profile reconstruction method based on a singular value decomposition (SVD) to extract the weak polarized line profiles. One of the key features of the line profile reconstruction is that the SVD line profiles are amenable to radiative transfer modeling within our Zeeman-Doppler Imaging code iMap. The code also utilizes a new iterative regularization scheme which is independent of any additional surface constraints. To provide more stability a vital part of our inversion strategy is the inversion of both Stokes I and Stokes V profiles to simultaneously reconstruct the temperature and magnetic field surface distribution of V410 Tau. A new image-shear analysis is also implemented to allow the search for image and line profile distortions induced by a differential rotation of the star. The magnetic field structure we obtain for V410 Tau shows a good spatial correlation with the surface temperature and is dominated by a strong field within the cool polar spot. The Zeeman-Doppler maps exhibit a large-scale organization of both polarities around the polar cap in the form of a twisted bipolar structure. The magnetic field reaches a value of almost 2 kG within the polar region but smaller fields are also present down to lower latitudes. The pronounced non-axisymmetric field structure and the non-detection of a differential rotation for V410 Tau supports the idea of an underlying $\alpha^2$-type dynamo, which is predicted for weak-lined T Tauri stars.Comment: Accepted for A&A, 18 pages, 10 figure

A closed-form solution to estimate uncertainty in non-rigid structure from motion

Semi-Definite Programming (SDP) with low-rank prior has been widely applied in Non-Rigid Structure from Motion (NRSfM). Based on a low-rank constraint, it avoids the inherent ambiguity of basis number selection in conventional base-shape or base-trajectory methods. Despite the efficiency in deformable shape reconstruction, it remains unclear how to assess the uncertainty of the recovered shape from the SDP process. In this paper, we present a statistical inference on the element-wise uncertainty quantification of the estimated deforming 3D shape points in the case of the exact low-rank SDP problem. A closed-form uncertainty quantification method is proposed and tested. Moreover, we extend the exact low-rank uncertainty quantification to the approximate low-rank scenario with a numerical optimal rank selection method, which enables solving practical application in SDP based NRSfM scenario. The proposed method provides an independent module to the SDP method and only requires the statistic information of the input 2D tracked points. Extensive experiments prove that the output 3D points have identical normal distribution to the 2D trackings, the proposed method and quantify the uncertainty accurately, and supports that it has desirable effects on routinely SDP low-rank based NRSfM solver.Comment: 9 pages, 2 figure