Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.

Spectral Clustering with Jensen-type kernels and their multi-point extensions

Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multipoint' kernels, and study their applications. We study a class of kernels based on Jensen type divergences and show that these can be extended to measure similarity among multiple points. We study tensor flattening methods and develop a multi-point (kernel) spectral clustering (MSC) method. We further emphasize on a special case of the proposed kernels, which is a multi-point extension of the linear (dot-product) kernel and show the existence of cubic time tensor flattening algorithm in this case. Finally, we illustrate the usefulness of our contributions using standard data sets and image segmentation tasks.

Prospects of zero Schottky barrier height in a graphene-inserted MoS2-metal interface

A low Schottky barrier height (SBH) at source/drain contact is essential for achieving high drive current in atomic layer MoS(2-)channel-based field effect transistors. Approaches such as choosing metals with appropriate work functions and chemical doping are employed previously to improve the carrier injection from the contact electrodes to the channel and to mitigate the SBH between the MoS2 and metal. Recent experiments demonstrate significant SBH reduction when graphene layer is inserted between metal slab (Ti and Ni) and MoS2. However, the physical or chemical origin of this phenomenon is not yet clearly understood. In this work, density functional theory simulations are performed, employing pseudopotentials with very high basis sets to get insights of the charge transfer between metal and monolayer MoS2 through the inserted graphene layer. Our atomistic simulations on 16 different interfaces involving five different metals (Ti, Ag, Ru, Au, and Pt) reveal that (i) such a decrease in SBH is not consistent among various metals, rather an increase in SBH is observed in case of Au and Pt; (ii) unlike MoS2-metal interface, the projected dispersion of MoS2 remains preserved in any MoS2-graphene- metal system with shift in the bands on the energy axis. (iii) A proper choice of metal (e.g., Ru) may exhibit ohmic nature in a graphene-inserted MoS2-metal contact. These understandings would provide a direction in developing high-performance transistors involving heteroatomic layers as contact electrodes. (c) 2016 AIP Publishing LLC.

Partition Energy Absorption of Axially Crushed Aluminum Foam-Filled Hat Sections

The "interaction effect" between aluminum foam and metal column that takes place when foam-filled hat sections (top-hats and double-hats) are axially crushed was investigated in this paper. Based on experimental examination, numerical simulation and analytical models, a systemic approach was developed to partition the energy absorption quantitatively into the foam filler component and the hat section component, and the relative contribution of each component to the overall interaction effect was therefore evaluated. Careful observation of the collapse profile found that the crushed foam filler could be further divided into two main energy-dissipation regions: densified region and extremely densified region. The volume reduction and volumetric strain of each region were empirically estimated. An analytical model pertinent to the collapse profile was thereafter proposed to find the more precise relationship between the volume reduction and volumetric strain of the foam filler. Combined the superfolding element model for hat sections with the current model according to the coupled method, each component energy absorption was subsequently derived, and the influence of some controlling factors was discussed. According to the finite element analysis and the theoretical modeling, when filled with foam, energy absorption was found to be increased both in the hat section and the foam filler, whereas the latter contributes predominantly to the interaction effect. The formation of the extremely densified region in the foam filler accounts for this effect.

Does spatial clustering help explaining differences in the inequality of income distribution? : evidence from Argentina

Resumen: Este artículo analiza la relación entre la agrupación espacial de la distribución del ingreso y la desigualdad en las provincias de Argentina. El objetivo de este trabajo es usar técnicas espaciales para analizar hasta que punto la agrupación espacial de la distribución del ingreso afecta la desigualdad de la distribución del ingreso en un contexto regional de Argentina. En general, la literatura de desigualdad implícitamente considera a cada región o provincia como una entidad independiente y el potencial para la observación de la interacción a través del espacio a menudo se ha ignorado. Mientras tanto, la autocorrelación espacial ocurre cuando la distribución espacial de la variable de interés exhibe un patrón sistemático. Yo computo tres medidas de autocorrelación espacial global: La I de Moran, c de Geary, y G de Getis y Ord, como grado de CLUSTERING provincial entre 1991 y 2002. La principal conclusión del trabajo es que hay evidencia que provincias con desigualdad relativamente alta (baja) tienden a ser localizadas cerca de otras provincias con alta (baja) desigualdad más a menudo de lo esperado debido al azar. Por ende cada provincia no debería ser vista como una observación independiente, como ha sido supuesto implícitamente en estudios previos sobre la desigualdad de ingresos regional.