Effect of p-d hybridization and structural distortion on the electronic properties of AgAlM2 (M = S, Se, Te) chalcopyrite semiconductors

We have carried out ab-initio calculation and study of structural and electronic properties of AgAlM2 (M = S, Se, Te) chalcopyrite semiconductors using Density Functional Theory (DFT) based self consistent Tight binding Linear Muffin Tin orbital (TB-LMTO) method. Calculated equlibrium values of lattice constants, anion displacement parameter (u), tetragonal distortion ({\eta} = c/2a) and bond lengths have good agreement with experimental values. Our study suggests these semiconductors to be direct band gap semiconductors with band gaps 1.98 eV, 1.59 eV and 1.36 eV respectively. These are in good agreement with experimental value within the limitation of local density approximation (LDA). Our explicit study of the effects of anion displacement and p-d hybridization show that band gap increases by 9.8%, 8.2% and 5.1% respectively for AgAlM2 (M = S, Se, Te) due to former effect and decreases by 51%, 47% and 42% respectively due to later effect.Comment: 15 pages, 17 figures, This article has been communicated to Solid State Communication

Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.Comment: Published in ECCV16. See the project website http://3drepresentation.stanford.edu/ and dataset website https://github.com/amir32002/3D_Street_Vie

[Letter to the Editor] Isolation of mitochondria is necessary for precise quantification of mitochondrial DNA damage in human carcinoma samples

Address correspondence to Carlo Vascotto, Department of Medical and Biological Sciences, University of Udine, Udine, 33100, Italy. E-mail: [email protected]

Dyonic Non-Abelian Black Holes

We study static spherically symmetric dyonic black holes in Einstein-Yang-Mills-Higgs theory. As for the magnetic non-abelian black holes, the domain of existence of the dyonic non-abelian black holes is limited with respect to the horizon radius and the dimensionless coupling constant $\alpha$, which is proportional to the ratio of vector meson mass and Planck mass. At a certain critical value of this coupling constant, $\hat \alpha$, the maximal horizon radius is attained. We derive analytically a relation between $\hat \alpha$ and the charge of the black hole solutions and confirm this relation numerically. Besides the fundamental dyonic non-abelian black holes, we study radially excited dyonic non-abelian black holes and globally regular gravitating dyons.Comment: LaTeX, 22 pages, 16 figures, three figures added, file manipulation error in previous replac

Determining Context Factors for Hybrid Development Methods with Trained Models

Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiarities of context factors influencing method and practice selection. In this paper, we utilize exploratory factor analysis and logistic regression analysis to learn such context factors and to identify methods that are correlated with these factors. Our analysis is based on 829 data points from the HELENA dataset. We provide five base clusters of methods consisting of up to 10 methods that lay the foundation for devising hybrid development methods. The analysis of the five clusters using trained models reveals only a few context factors, e.g., project/product size and target application domain, that seem to significantly influence the selection of methods. An extended descriptive analysis of these practices in the context of the identified method clusters also suggests a consolidation of the relevant practice sets used in specific project contexts