Four dimensions characterize comprehensive trait judgments of faces

People readily attribute many traits to faces: some look beautiful, some competent, some aggressive. These snap judgments have important consequences in real life, ranging from success in political elections to decisions in courtroom sentencing. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance, a highly influential framework that has been the basis for numerous studies in social and developmental psychology, social neuroscience, and in engineering applications. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments, and reconcile prior work on face perception with work in social cognition and personality psychology

Disorder-free localization around the conduction band edge of crossing and kinked silicon nanowires

We explore ballistic regime quantum transport characteristics of oxide-embedded crossing and kinked silicon nanowires (NWs) within a large-scale empirical pseudopotential electronic structure framework, coupled to the Kubo-Greenwood transport analysis. A real-space wave function study is undertaken and the outcomes are interpreted together with the findings of ballistic transport calculations. This reveals that ballistic transport edge lies tens to hundreds of millielectron volts above the lowest unoccupied molecular orbital, with a substantial number of localized states appearing in between, as well as above the former. We show that these localized states are not due to the oxide interface, but rather core silicon-derived. They manifest the wave nature of electrons brought to foreground by the reflections originating from NW junctions and bends. Hence, we show that the crossings and kinks of even ultraclean Si NWs possess a conduction band tail without a recourse to atomistic disorder.Comment: Published version, 7 pages, 9 figure

Networks of silicon nanowires: a large-scale atomistic electronic structure analysis

Networks of silicon nanowires possess intriguing electronic properties surpassing the predictions based on quantum confinement of individual nanowires. Employing large-scale atomistic pseudopotential computations, as yet unexplored branched nanostructures are investigated in the subsystem level, as well as in full assembly. The end product is a simple but versatile expression for the bandgap and band edge alignments of multiply-crossing Si nanowires for various diameters, number of crossings, and wire orientations. Further progress along this line can potentially topple the bottom-up approach for Si nanowire networks to a top-down design by starting with functionality and leading to an enabling structure.Comment: Published version, 5+2 pages (including supplementary material

Silicon nanowire-based complex structures : A Large-scale atomistic electronic structure and ballistic transport

Ankara : The Department of Physics and the Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Ph. D.) -- Bilkent University, 2014.Includes bibliographical references leaves 94-112.While the hierarchical assembling as well as the dramatic miniaturization of Si nanowires (NWs) are on-going, an understanding of the underlying physics is of great importance to enable custom design of nanostructures tailored to specific functionalities. This work presents a large-scale atomistic insight into the electronic properties of NW-based complex structures, starting from the subsystem level up to the full assembly, within the framework of pseudopotential-based linear combination of bulk bands method. Laying the groundwork by grasping single Si NWs, we get into a large extent an unexplored territory of NW networks and kinked NWs. As one end product, a versatile estimator is introduced for the band gap and band-edge lineups of multiply-crossing Si NWs that is valid for various diameters, number of crossings, and NW alignments. Aiming for an exploration of the low-lying energy landscape, real space wave function analysis is undertaken for tens of states around band edges which reveal underlying features for a variety of crossings. Predominantly, the valence states spread throughout the network, in contrast the conduction minima are largely localized at the crossings. Given the fact that substantial portion of the band edge shift drives from the confined conduction states, branched Si NWs and nanocrystals have quite close band gap values as the networks of similar wire diameters. Further support to wave function analysis is provided via quantum ballistic transport calculations employing the Kubo-Greenwood formalism. The intriguing localization behaviors are identified, springing mainly at the crossings and kinks of NWs. The ballistic transport edge set apart the conducting extended states from the localized-band gap determining ones. Our findings put forward useful information to realize functionality encoded synthesis of NW-based complex structures, both in the bottom-up and top-down fabrication paradigms.Keleş, ÜmitPh.D

Slow light in Germanium nanocrystals

Ankara : The Department of Physics and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 53-56.The phenomena of quantum coherence has been applied with great success in the atomic systems. For optoelectronic applications the interest is inherently directed towards the semiconductor heterostructures. Large number of works have proposed and analyzed the atomic quantum coherence effects in the semiconductors. In this respect, nanocrystals (NCs) are very promising structures for seeking the quantum coherence phenomena due to their atomic-like electronic structure. Furthermore, their robust structure, integrability and larger excitonic lifetimes with respect to atomic systems makes them more promising candidates for the technological applications. Within an atomistic pseudopotential electronic structure framework, the optical Bloch equations (OBEs) originating from atomic coherence theory are derived and solved numerically for Ge NCs. The results are interpreted in the context of coherent population oscillations (CPO). Narrow dips are observed in the absorption profiles which corresponds to high dispersions within a transparency window and produce slow light. A systematic study of the size-scaling of slow-down factor with respect to NC diameter and controllable slow light by applying external Stark field are provided. The results indicate that Ge NCs can be used to generate optically and electrically controllable slow light. The many-body Coulomb interactions which underlie the quantum coherence and dephasing are of central importance in semiconductor quantum confined systems. The effects of many-body interactions on the optical response of Ge NCs have been analyzed. The semiconductor optical Bloch equations (SBEs) are derived in a semiclassical approach and the Coulomb correlations are included at the level of Hartree-Fock approximation.Keleş, ÜmitM.S

Efficient prediction of trait judgments from faces using deep neural networks

Judgments of people from their faces are often invalid but influence many social decisions (e.g., legal sentencing), making them an important target for automated prediction. Direct training of deep convolutional neural networks (DCNNs) is difficult because of sparse human ratings, but features obtained from DCNNs pre-trained on other classifications (e.g., object recognition) can predict trait judgments within a given face database. However, it remains unknown if this latter approach generalizes across faces, raters, or traits. Here we directly compare three distinct types of face features, and test them across multiple out-of-sample datasets and traits. DCNNs pre-trained on face identification provided features that generalized the best, and models trained to predict a given trait also predicted several other traits. We demonstrate the flexibility, generalizability, and efficiency of using DCNN features to predict human trait judgments from faces, providing an easily scalable framework for automated prediction of human judgment

Spatially informed voxelwise modeling for naturalistic fMRI experiments

Voxelwise modeling (VM) is a powerful framework to predict single voxel responses evoked by a rich set of stimulus features present in complex natural stimuli. However, because VM disregards correlations across neighboring voxels, its sensitivity in detecting functional selectivity can be diminished in the presence of high levels of measurement noise. Here, we introduce spatially-informed voxelwise modeling (SPIN-VM) to take advantage of response correlations in spatial neighborhoods of voxels. To optimally utilize shared information, SPIN-VM performs regularization across spatial neighborhoods in addition to model features, while still generating single-voxel response predictions. We demonstrated the performance of SPIN-VM on a rich dataset from a natural vision experiment. Compared to VM, SPIN-VM yields higher prediction accuracies and better capture locally congruent information representations across cortex. These results suggest that SPIN-VM offers improved performance in predicting single-voxel responses and recovering coherent information representations

Spatially informed voxelwise modeling for naturalistic fMRI experiments

Voxelwise modeling (VM) is a powerful framework to predict single voxel responses evoked by a rich set of stimulus features present in complex natural stimuli. However, because VM disregards correlations across neighboring voxels, its sensitivity in detecting functional selectivity can be diminished in the presence of high levels of measurement noise. Here, we introduce spatially-informed voxelwise modeling (SPIN-VM) to take advantage of response correlations in spatial neighborhoods of voxels. To optimally utilize shared information, SPIN-VM performs regularization across spatial neighborhoods in addition to model features, while still generating single-voxel response predictions. We demonstrated the performance of SPIN-VM on a rich dataset from a natural vision experiment. Compared to VM, SPIN-VM yields higher prediction accuracies and better capture locally congruent information representations across cortex. These results suggest that SPIN-VM offers improved performance in predicting single-voxel responses and recovering coherent information representations