Classification calibration dimension for general multiclass losses

We study consistency properties of surrogate loss functions for general multiclass classification problems, defined by a general loss matrix. We extend the notion of classification calibration, which has been studied for binary and multiclass 0-1 classification problems (and for certain other specific learning problems), to the general multiclass setting, and derive necessary and sufficient conditions for a surrogate loss to be classification calibrated with respect to a loss matrix in this setting. We then introduce the notion of \emph{classification calibration dimension} of a multiclass loss matrix, which measures the smallest `size' of a prediction space for which it is possible to design a convex surrogate that is classification calibrated with respect to the loss matrix. We derive both upper and lower bounds on this quantity, and use these results to analyze various loss matrices. In particular, as one application, we provide a different route from the recent result of Duchi et al.\ (2010) for analyzing the difficulty of designing `low-dimensional' convex surrogates that are consistent with respect to pairwise subset ranking losses. We anticipate the classification calibration dimension may prove to be a useful tool in the study and design of surrogate losses for general multiclass learning problems.

Melting and solidification behavior of Pb-Sn embedded alloy nano-particles

Nano-sized bimetallic dispersoids consisting of (Pb) and beta-(Sn) phases of eutectic composition (Pb26.1Sn73.9) embedded in aluminum and Al-Cu-Fe quasicrystalline matrices have been prepared by rapid solidification processing. The two phases, face centered cubic (Pb) and body center tetragonal, beta-(Sn) solid solution co-exist in all the embedded nanoparticles at room temperature. The phases bear crystallographic orientation relationship with the matrix. In situ TEM study has been carried out for the alloy particles to study the melting and the solidification behavior. The detailed microscopic observations indicate formation of a single-phase metastable fcc (Pb) in the nano-particles prior to the melting during heating. Solidification of these particles begins with nucleation of fcc (Pb), which phase separates into fcc (Pb) and beta-(Sn) lamellae in the solid state. In situ X-ray diffraction study is carried out to obtain lattice parameter of metastable fcc (Pb) and thereby an estimate of amount of Sn dissolved in the metastable (Pb) prior to the melting. The results are discussed in terms of a metastable phase diagram between fcc Pb and fcc Sn and invoking the size effect on the metastable phase diagram. The size factor is found to play a critical role in deciding the pathway of phase transformation as well as the extension of solid solubility of Sn in fcc (Pb) in the nano-particles.

Eigen-profiles of spatio-temporal fragments for adaptive region-based tracking

We propose a novel space-time descriptor for region-based tracking which is very concise and efficient. The regions represented by covariance matrices within a temporal fragment, are used to estimate this space-time descriptor which we call the Eigenprofiles(EP). EP so obtained is used in estimating the Covariance Matrix of features over spatio-temporal fragments. The Second Order Statistics of spatio-temporal fragments form our target model which can be adapted for variations across the video. The model being concise also allows the use of multiple spatially overlapping fragments to represent the target. We demonstrate good tracking results on very challenging datasets, shot under insufficient illumination conditions.

Minimizing the Complexity of Fast Sphere Decoding of STBCs

Decoding of linear space-time block codes (STBCs) with sphere-decoding (SD) is well known. A fast-version of the SD known as fast sphere decoding (FSD) was introduced by Biglieri, Hong and Viterbo. Viewing a linear STBC as a vector space spanned by its defining weight matrices over the real number field, we define a quadratic form (QF), called the Hurwitz-Radon QF (HRQF), on this vector space and give a QF interpretation of the FSD complexity of a linear STBC. It is shown that the FSD complexity is only a function of the weight matrices defining the code and their ordering, and not of the channel realization (even though the equivalent channel when SD is used depends on the channel realization) or the number of receive antennas. It is also shown that the FSD complexity is completely captured into a single matrix obtained from the HRQF. Moreover, for a given set of weight matrices, an algorithm to obtain an optimal ordering of them leading to the least FSD complexity is presented. The well known classes of low FSD complexity codes (multi-group decodable codes, fast decodable codes and fast group decodable codes) are presented in the framework of HRQF.

Curvature based mobility analysis and form closure of smooth planar curves with multiple contacts

This paper presents a simple second-order, curvature based mobility analysis of planar curves in contact. The underlying theory deals with penetration and separation of curves with multiple contacts, based on relative configuration of osculating circles at points of contact for a second-order rotation about each point of the plane. Geometric and analytical treatment of mobility analysis is presented for generic as well as special contact geometries. For objects with a single contact, partitioning of the plane into four types of mobility regions has been shown. Using point based composition operations based on dual-number matrices, analysis has been extended to computationally handle multiple contacts scenario. A novel color coded directed line has been proposed to capture the contact scenario. Multiple contacts mobility is obtained through intersection of the mobility half-spaces. It is derived that mobility region comprises a pair of unbounded or a single bounded convex polygon. The theory has been used for analysis and synthesis of form closure configurations, revolute and prismatic kinematic pairs. (C) 2013 Elsevier Ltd. All rights reserved.

A finite element method for the Saint-Venant torsion and bending problems for prismatic beams

In this work, we present a finite element formulation for the Saint-Venant torsion and bending problems for prismatic beams. The torsion problem formulation is based on the warping function, and can handle multiply-connected regions (including thin-walled structures), compound and anisotropic bars. Similarly, the bending formulation, which is based on linearized elasticity theory, can handle multiply-connected domains including thin-walled sections. The torsional rigidity and shear centers can be found as special cases of these formulations. Numerical results are presented to show the good coarse-mesh accuracy of both the formulations for both the displacement and stress fields. The stiffness matrices and load vectors (which are similar to those for a variable body force in a conventional structural mechanics problem) in both formulations involve only domain integrals, which makes them simple to implement and computationally efficient. (C) 2014 Elsevier Ltd. All rights reserved.

THE KNICKKOPF DOMON DOMAIN IS ESSENTIAL FOR CUTICLE DIFFERENTIATION IN Drosophila melanogaster

The dopamine monoxygenase N-terminal (DOMON) domain is found in extracellular proteins across several eukaryotic and prokaryotic taxa. It has been proposed that this domain binds to heme or sugar moieties. Here, we have analyzed the role of four highly conserved amino acids in the DOMON domain of the Drosophila melanogaster Knickkopf protein that is inserted into the apical plasma membrane and assists extracellular chitin organization. In principal, we generated Knickkopf versions with exchanged residues tryptophan(299,) methionine(333), arginine(401), or histidine(437), and scored for the ability of the respective engineered protein to normalize the knickkopf mutant phenotype. Our results confirm the absolute necessity of tryptophan(299,) methionine(333), and histidine(437) for Knickkopf function and stability, the latter two being predicted to be critical for heme binding. In contrast, arginine(401) is required for full efficiency of Knickkopf activity. Taken together, our genetic data support the prediction of these residues to mediate the function of Knickkopf during cuticle differentiation in insects. Hence, the DOMON domain is apparently an essential factor contributing to the construction of polysaccharide-based extracellular matrices.

Automatic finite element formulation and assembly of hyperelastic higher order structural models

The formulation of higher order structural models and their discretization using the finite element method is difficult owing to their complexity, especially in the presence of non-linearities. In this work a new algorithm for automating the formulation and assembly of hyperelastic higher-order structural finite elements is developed. A hierarchic series of kinematic models is proposed for modeling structures with special geometries and the algorithm is formulated to automate the study of this class of higher order structural models. The algorithm developed in this work sidesteps the need for an explicit derivation of the governing equations for the individual kinematic modes. Using a novel procedure involving a nodal degree-of-freedom based automatic assembly algorithm, automatic differentiation and higher dimensional quadrature, the relevant finite element matrices are directly computed from the variational statement of elasticity and the higher order kinematic model. Another significant feature of the proposed algorithm is that natural boundary conditions are implicitly handled for arbitrary higher order kinematic models. The validity algorithm is illustrated with examples involving linear elasticity and hyperelasticity. (C) 2013 Elsevier Inc. All rights reserved.

Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition

A new representation of spatio-temporal random processes is proposed in this work. In practical applications, such processes are used to model velocity fields, temperature distributions, response of vibrating systems, to name a few. Finding an efficient representation for any random process leads to encapsulation of information which makes it more convenient for a practical implementations, for instance, in a computational mechanics problem. For a single-parameter process such as spatial or temporal process, the eigenvalue decomposition of the covariance matrix leads to the well-known Karhunen-Loeve (KL) decomposition. However, for multiparameter processes such as a spatio-temporal process, the covariance function itself can be defined in multiple ways. Here the process is assumed to be measured at a finite set of spatial locations and a finite number of time instants. Then the spatial covariance matrix at different time instants are considered to define the covariance of the process. This set of square, symmetric, positive semi-definite matrices is then represented as a third-order tensor. A suitable decomposition of this tensor can identify the dominant components of the process, and these components are then used to define a closed-form representation of the process. The procedure is analogous to the KL decomposition for a single-parameter process, however, the decompositions and interpretations vary significantly. The tensor decompositions are successfully applied on (i) a heat conduction problem, (ii) a vibration problem, and (iii) a covariance function taken from the literature that was fitted to model a measured wind velocity data. It is observed that the proposed representation provides an efficient approximation to some processes. Furthermore, a comparison with KL decomposition showed that the proposed method is computationally cheaper than the KL, both in terms of computer memory and execution time.

Perovskite ceramic nanoparticles in polymer composites for augmenting bone tissue regeneration

There is increasing interest in the use of nanoparticles as fillers in polymer matrices to develop biomaterials which mimic the mechanical, chemical and electrical properties of bone tissue for orthopaedic applications. The objective of this study was to prepare poly(epsilon-caprolactone) (PCL) nanocomposites incorporating three different perovskite ceramic nanoparticles, namely, calcium titanate (CT), strontium titanate (ST) and barium titanate (BT). The tensile strength and modulus of the composites increased with the addition of nanoparticles. Scanning electron microscopy indicated that dispersion of the nanoparticles scaled with the density of the ceramics, which in turn played an important role in determining the enhancement in mechanical properties of the composite. Dielectric spectroscopy revealed improved permittivity and reduced losses in the composites when compared to neat PCL. Nanofibrous scaffolds were fabricated via electrospinning. Induction coupled plasma-optical emission spectroscopy indicated the release of small quantities of Ca+2, Sr+2, Ba+2 ions from the scaffolds. Piezo-force microscopy revealed that BT nanoparticles imparted piezoelectric properties to the scaffolds. In vitro studies revealed that all composites support osteoblast proliferation. Expression of osteogenic genes was enhanced on the nanocomposites in the following order: PCL/CT>PCL/ST>PCL/BT>PCL. This study demonstrates that the use of perovskite nanoparticles could be a promising technique to engineer better polymeric scaffolds for bone tissue engineering.

An Eigen Approach to Transmit Beamforming in Wireless Networks With Single Antenna Receivers

In this paper, we propose an eigen framework for transmit beamforming for single-hop and dual-hop network models with single antenna receivers. In cases where number of receivers is not more than three, the proposed Eigen approach is vastly superior in terms of ease of implementation and computational complexity compared with the existing convex-relaxation-based approaches. The essential premise is that the precoding problems can be posed as equivalent optimization problems of searching for an optimal vector in the joint numerical range of Hermitian matrices. We show that the latter problem has two convex approximations: the first one is a semi-definite program that yields a lower bound on the solution, and the second one is a linear matrix inequality that yields an upper bound on the solution. We study the performance of the proposed and existing techniques using numerical simulations.

Formulation of nanotized curcumin and demonstration of its antimalarial efficacy

Aim: The present study was conducted to overcome the disadvantages associated with the poor water solubility and low bioavailability of curcumin by synthesizing nanotized curcumin and demonstrating its efficacy in treating malaria. Materials and methods: Nanotized curcumin was prepared by a modified emulsion-diffusion-evaporation method and was characterized by means of transmission electron microscopy, atomic force microscopy, dynamic light scattering, Zetasizer, Fourier transform infrared spectroscopy, and differential thermal analysis. The novelty of the prepared nanoformulation lies in the fact that it was devoid of any polymeric matrices used in conventional carriers. The antimalarial efficacy of the prepared nanotized curcumin was then checked both in vitro and in vivo. Results: The nanopreparation was found to be non-toxic and had a particle size distribution of 20-50 nm along with improved aqueous dispersibility and an entrapment efficiency of 45%. Nanotized curcumin (half maximal inhibitory concentration IC50]: 0.5 mu M) was also found to be ten-fold more effective for growth inhibition of Plasmodium falciparum in vitro as compared to its native counterpart (IC50: 5 mu M). Oral bioavailability of nanotized curcumin was found to be superior to that of its native counterpart. Moreover, when Plasmodium berghei-infected mice were orally treated with nanotized curcumin, it prolonged their survival by more than 2 months with complete clearance of parasites in comparison to the untreated animals, which survived for 8 days only. Conclusion: Nanotized curcumin holds a considerable promise in therapeutics as demonstrated here for treating malaria as a test system.

Riemann shock tube: 1D normal shocks in air, simulations and experiments

This paper presents numerical simulation of the evolution of one-dimensional normal shocks, their propagation, reflection and interaction in air using a single diaphragm Riemann shock tube and validate them using experimental results. Mathematical model is derived for one-dimensional compressible flow of viscous and conducting medium. Dimensionless form of the mathematical model is used to construct space-time finite element processes based on minimization of the space-time residual functional. The space-time local approximation functions for space-time p-version hierarchical finite elements are considered in higher order GRAPHICS] spaces that permit desired order of global differentiability of local approximations in space and time. The resulting algebraic systems from this approach yield unconditionally positive-definite coefficient matrices, hence ensure unique numerical solution. The evolution is computed for a space-time strip corresponding to a time increment Delta t and then time march to obtain the evolution up to any desired value of time. Numerical studies are designed using recently invented hand-driven shock tube (Reddy tube) parameters, high/low side density and pressure values, high- and low-pressure side shock tube lengths, so that numerically computed results can be compared with actual experimental measurements.

Collective eigenstates of emission in an N-entity heterostructure and the evaluation of its Green tensors and self-energy components

Optical emission from emitters strongly interacting among themselves and also with other polarizable matter in close proximity has been approximated by emission from independent emitters. This is primarily due to our inability to evaluate the self-energy matrices and radiative properties of the collective eigenstates of emitters in heterogeneous ensembles. A method to evaluate self-energy matrices that is not limited by the geometry and material composition is presented to understand and exploit such collective excitations. Numerical evaluations using this method are used to highlight the significant differences between independent and the collective modes of emission in nanoscale heterostructures. A set of N Lorentz emitters and other polarizable entities is used to represent the coupled system of a generalized geometry in a volume integral approach. Closed form relations between the Green tensors of entity pairs in free space and their correspondents in a heterostructure are derived concisely. This is made possible for general geometries because the global matrices consisting of all free-space Green dyads are subject to conservation laws. The self-energy matrix can then be assembled using the evaluated Green tensors of the heterostructure, but a decomposition of its components into their radiative and nonradiative decay contributions is nontrivial. The relations to compute the observables of the eigenstates (such as quantum efficiency, power/energy of emission, radiative and nonradiative decay rates) are presented. A note on extension of this method to collective excitations, which also includes strong interactions with a surface in the near-field, is added. (C) 2014 Optical Society of America

Hardware Accelerator for 3D Method of Moments based Parasitic Extraction

A Field Programmable Gate Array (FPGA) based hardware accelerator for multi-conductor parasitic capacitance extraction, using Method of Moments (MoM), is presented in this paper. Due to the prohibitive cost of solving a dense algebraic system formed by MoM, linear complexity fast solver algorithms have been developed in the past to expedite the matrix-vector product computation in a Krylov sub-space based iterative solver framework. However, as the number of conductors in a system increases leading to a corresponding increase in the number of right-hand-side (RHS) vectors, the computational cost for multiple matrix-vector products present a time bottleneck, especially for ill-conditioned system matrices. In this work, an FPGA based hardware implementation is proposed to parallelize the iterative matrix solution for multiple RHS vectors in a low-rank compression based fast solver scheme. The method is applied to accelerate electrostatic parasitic capacitance extraction of multiple conductors in a Ball Grid Array (BGA) package. Speed-ups up to 13x over equivalent software implementation on an Intel Core i5 processor for dense matrix-vector products and 12x for QR compressed matrix-vector products is achieved using a Virtex-6 XC6VLX240T FPGA on Xilinx's ML605 board.