A finite-rate-of-innovation signal sampling perspective of the source-filter model of speech production

We consider the speech production mechanism and the asso- ciated linear source-filter model. For voiced speech sounds in particular, the source/glottal excitation is modeled as a stream of impulses and the filter as a cascade of second-order resonators. We show that the process of sampling speech signals can be modeled as filtering a stream of Dirac impulses (a model for the excitation) with a kernel function (the vocal tract response),and then sampling uniformly. We show that the problem of esti- mating the excitation is equivalent to the problem of recovering a stream of Dirac impulses from samples of a filtered version. We present associated algorithms based on the annihilating filter and also make a comparison with the classical linear prediction technique, which is well known in speech analysis. Results on synthesized as well as natural speech data are presented.

Geometry of static balancing during posture transition

The interaction between the digital human model (DHM) and environment typically occurs in two distinct modes; one, when the DHM maintains contacts with the environment using its self weight, wherein associated reaction forces at the interface due to gravity are unidirectional; two, when the DHM applies both tension and compression on the environment through anchoring. For static balancing in first mode of interaction, it is sufficient to maintain the projection of the centre of mass (COM) inside the convex region induced by the weight supporting segments of the body on a horizontal plane. In DHM, static balancing is required while performing specified tasks such as reach, manipulation and locomotion; otherwise the simulations would not be realistic. This paper establishes the geometric relationships that must be satisfied for maintaining static balance while altering the support configurations for a given posture and altering the posture for a given support condition. For a given location of the COM for a system supported by multiple point contacts, the conditions for simultaneous withdrawal of a specified set of contacts have been determined in terms of the convex hulls of the subsets of the points of contact. When the projection of COM must move beyond the existing support for performing some task, new supports must be enabled for maintaining static balance. This support seeking behavior could also manifest while planning for reduction of support stresses. Feasibility of such a support depends upon the availability of necessary features in the environment. Geometric conditions necessary for selection of new support on horizontal,inclined and vertical surfaces within the workspace of the DHM for such dynamic scenario have been derived. The concepts developed are demonstrated using the cases of sit-to-stand posture transition for manipulation of COM within the convex supporting polygon, and statically stable walking gaits for support seeking within the kinematic capabilities of the DHM. The theory developed helps in making the DHM realize appropriate behaviors in diverse scenarios autonomously.

IceCube: efficient targeted mining in data cubes

We address the problem of mining targeted association rules over multidimensional market-basket data. Here, each transaction has, in addition to the set of purchased items, ancillary dimension attributes associated with it. Based on these dimensions, transactions can be visualized as distributed over cells of an n-dimensional cube. In this framework, a targeted association rule is of the form {X -> Y} R, where R is a convex region in the cube and X. Y is a traditional association rule within region R. We first describe the TOARM algorithm, based on classical techniques, for identifying targeted association rules. Then, we discuss the concepts of bottom-up aggregation and cubing, leading to the CellUnion technique. This approach is further extended, using notions of cube-count interleaving and credit-based pruning, to derive the IceCube algorithm. Our experiments demonstrate that IceCube consistently provides the best execution time performance, especially for large and complex data cubes.

Borocarbonitrides, BxCyNz

Various forms of carbon, especially the nanocarbons, have received considerable attention in recent years. There has also been some effort to investigate borocarbonitrides, BxCyNz, comprising besides carbon, the two elements on either side. Although uniformly homogeneous compositions of borocarbonitrides may be difficult to generate, there have been attempts to prepare them by solid state as well as gas phase reactions. Some of the products so obtained show evidence for the presence of BCN networks. Then, there are composites (G-BN) containing hexagonal BN (h-BN) and graphene (G) domains, G(1-x)(BN)(x), in varying proportions. Nanotubes of BxCyNz have been reported by several workers. The borocarbonitrides exhibit some interesting electronic and gas adsorption properties. Thus, some of the preparations show selective CO2 adsorption. They also exhibit excellent characteristics for supercapacitor applications. In order to understand the nature of these understudied materials, it is necessary to examine the results from first-principles calculations. These calculations throw light on the variation in the band gap of G-BN with the concentration of h-BN, for different geometries of the domains and their boundaries. The possibility of formation of Stone-Wales (SW) defects at the interfaces of graphene and h-BN has been studied and the estimates of the formation energies of SW defects at the interfaces are similar to 4 to 6 eV. The presence of such defects at the interfaces influences the electronic structure near the band gap and the associated properties. For example, adsorption of CH4 and CO2 occurs with significantly stronger binding at the interfacial defects.

Robust formulations for clustering-based large-scale classification

Chebyshev-inequality-based convex relaxations of Chance-Constrained Programs (CCPs) are shown to be useful for learning classifiers on massive datasets. In particular, an algorithm that integrates efficient clustering procedures and CCP approaches for computing classifiers on large datasets is proposed. The key idea is to identify high density regions or clusters from individual class conditional densities and then use a CCP formulation to learn a classifier on the clusters. The CCP formulation ensures that most of the data points in a cluster are correctly classified by employing a Chebyshev-inequality-based convex relaxation. This relaxation is heavily dependent on the second-order statistics. However, this formulation and in general such relaxations that depend on the second-order moments are susceptible to moment estimation errors. One of the contributions of the paper is to propose several formulations that are robust to such errors. In particular a generic way of making such formulations robust to moment estimation errors is illustrated using two novel confidence sets. An important contribution is to show that when either of the confidence sets is employed, for the special case of a spherical normal distribution of clusters, the robust variant of the formulation can be posed as a second-order cone program. Empirical results show that the robust formulations achieve accuracies comparable to that with true moments, even when moment estimates are erroneous. Results also illustrate the benefits of employing the proposed methodology for robust classification of large-scale datasets.

Composite cyclodextrin-calcium carbonate porous microparticles and modified multilayer capsules: novel carriers for encapsulation of hydrophobic drugs

Novel composite cyclodextrin (CD)-CaCO3 spherical porous microparticles have been synthesized through Ca2+-CD complex formation, which influences the crystal growth of CaCO3. The CDs are entrapped and distributed uniformly in the matrix of CaCO3 microparticles during crystallization. The hydrophobic fluorescent molecules coumarin and Nile red (NR) are efficiently encapsulated into these composite CD-CaCO3 porous particles through supramolecular inclusion complexation between entrapped CDs and hydrophobic molecules. Thermogravimetric (TGA) and infrared spectroscopy (IR) analysis of composite CD-CaCO3 particles reveals the presence of large CDs and their strong interaction with calcium carbonate nanoparticles. The resulting composite CD-CaCO3 microparticles are utilized as sacrificial templates for preparation of CD-modified layer-by-layer (LbL) capsules. After dissolution of the carbonate core, CDs are retained in the interior of the capsules in a network fashion and assist in the encapsulation of hydrophobic molecules. The efficient encapsulation of the hydrophobic fluorescent dye, coumarin, was successfully demonstrated using CD-modified capsules. In vitro release of the encapsulated coumarin from the CD-CaCO3 and CD-modified capsules has been demonstrated.

Exchange-spring mechanism of soft and hard ferrite nanocomposites

The paper reports exchange-spring soft and hard ferrite nanocomposites synthesized by chemical co-precipitation with or without the application of ultrasonic vibration. The composites contained BaFe12O19 as the hard phase and CoFe2O4/MgFe2O4 as the soft phase. X-ray diffraction patterns of the samples in the optimum calcined condition indicated the presence of soft ferrites as face-centred cubic (fcc) and hard ferrites as hexagonal close packed (hcp) structure respectively. Temperature dependence of magnetization in the range of 20-700 degrees C demonstrated distinct presence of soft and hard ferrites as magnetic phases which are characterized by wide difference in magnetic anisotropy and coercivity. Exchange-spring mechanism led these nanocomposite systems to exchange-coupled, which ultimately produced convex hysteresis loops characteristic of a single-phase permanent magnet. Fairly high value of coercivity and maximum energy product were observed for the samples in the optimum calcined conditions with a maximum applied field of 1600 kA/m (2 T).

Bounds on the sum-rate capacity of the Gaussian MIMO X channel

We consider the MIMO X channel (XC), a system consisting of two transmit-receive pairs, where each transmitter communicates with both the receivers. Both the transmitters and receivers are equipped with multiple antennas. First, we derive an upper bound on the sum-rate capacity of the MIMO XC under individual power constraint at each transmitter. The sum-rate capacity of the two-user multiple access channel (MAC) that results when receiver cooperation is assumed forms an upper bound on the sum-rate capacity of the MIMO XC. We tighten this bound by considering noise correlation between the receivers and deriving the worst noise covariance matrix. It is shown that the worst noise covariance matrix is a saddle-point of a zero-sum, two-player convex-concave game, which is solved through a primal-dual interior point method that solves the maximization and the minimization parts of the problem simultaneously. Next, we propose an achievable scheme which employs dirty paper coding at the transmitters and successive decoding at the receivers. We show that the derived upper bound is close to the achievable region of the proposed scheme at low to medium SNRs.

Decode-and-forward relay beamforming for secrecy with imperfect CSI and multiple eavesdroppers

In this paper, we evaluate secrecy rates in cooperative relay beamforming in the presence of imperfect channel state information (CSI) and multiple eavesdroppers. A source-destination pair aided by.. out of.. relays, 1 <= k <= M, using decode-and-forward relay beamforming is considered. We compute the worst case secrecy rate with imperfect CSI in the presence of multiple eavesdroppers, where the number of eavesdroppers can be more than the number of relays. We solve the optimization problem for all possible relay combinations to find the secrecy rate and optimum source and relay weights subject to a total power constraint. We relax the rank-1 constraint on the complex semi-definite relay weight matrix and use S-procedure to reformulate the optimization problem that can be solved using convex semi-definite programming.

Effect of hybridizing micron-sized Ti with nano-sized SiC on the microstructural evolution and mechanical response of Mg-5.6Ti composite

In this work, the effect of hybridizing micro-Ti with nano-SiC particulates on the microstructural and the mechanical behaviour of Mg-5.6Ti composite were investigated. Mg materials containing micron-sized Ti particulates hybridized with different amounts of nano-size SiC particulates were synthesized using the disintegrated melt deposition method followed by hot extrusion. The microstructural and mechanical behaviour of the developed Mg hybrid composites were studied in comparison with Mg-5.6Ti. Microstructural characterization revealed grain refinement attributed to the presence of uniformly distributed micro-Ti particles embedded with nano-SiC particulates. Electron back scattered diffraction (EBSD) analyses of Mg-(5.6Ti + 1.0SiC)(BM) hybrid composite showed relatively more localized recrystallized grains and lesser tensile twin fraction, when compared to Mg-5.6Ti. The evaluation of mechanical properties indicated that the best combination of strength and ductility was observed in the Mg-(5.6Ti + 1.0SiC)(BM) hybrid composites. The superior strength properties of the Mg-(5.6Ti + x-SiC)(BM) hybrid composites when compared to Mg-5.6Ti is attributed to the presence of nano-reinforcements, the uniform distribution of the hybridized particles and the better interfacial bonding between the matrix and the reinforcement particles, achieved by nano-SiC addition.

Hybrid aggregated-vector algorithm for efficient parallelization of fast multipole method

An efficient parallelization algorithm for the Fast Multipole Method which aims to alleviate the parallelization bottleneck arising from lower job-count closer to root levels is presented. An electrostatic problem of 12 million non-uniformly distributed mesh elements is solved with 80-85% parallel efficiency in matrix setup and matrix-vector product using 60GB and 16 threads on shared memory architecture.

Scalar transport in diffusion flame wrapped up by an air and fuel side vortex

The present work involves a computational study of soot (chosen as a scalar which is a primary pollutant source) formation and transport in a laminar acetylene diffusion flame perturbed by a convecting line vortex. The topology of soot contours resulting from flame vortex interactions has been investigated. More soot was produced when vortex was introduced from the air side in comparison to the fuel side. Also, the soot topography was spatially more diffuse in the case of air side vortex. The computational model was found to be in good agreement with the experimental work previously reported in the literature. The computational simulation enabled a study of various parameters like temperature, equivalence ratio and temperature gradient affecting the soot production and transport. Temperatures were found to be higher in the case of air side vortex in contrast to the fuel side one. In case of fuel side vortex, abundance of fuel in the vortex core resulted in fuel-rich combustion zone in the core and a more discrete soot topography. Besides, the overall soot production was observed to be low in the fuel side vortex. However, for the air side vortex, air abundance in the core resulted in higher temperatures and greater soot production. Probability density functions (PDFs) have been introduced to investigate the spatiotemporal variation of soot yield and transport and their dependence on temperature and acetylene concentration from statistical view point. In addition, the effect of flame curvature on soot production is also studied. The regions convex to fuel stream side witnessed thicker soot layer. All numerical simulations have been carried out on Fluent 6.3.26. (C) 2013 Elsevier Ltd. All rights reserved.

On the Erdos-Szekeres n-interior-point problem

The n-interior-point variant of the Erdos Szekeres problem is the following: for every n, n >= 1, does there exist a g(n) such that every point set in the plane with at least g(n) interior points has a convex polygon containing exactly n interior points. The existence of g(n) has been proved only for n <= 3. In this paper, we show that for any fixed r >= 2, and for every n >= 5, every point set having sufficiently large number of interior points and at most r convex layers contains a subset with exactly n interior points. We also consider a relaxation of the notion of convex polygons and show that for every n, n >= 1, any point set with at least n interior points has an almost convex polygon (a simple polygon with at most one concave vertex) that contains exactly n interior points. (C) 2013 Elsevier Ltd. All rights reserved.

On piercing (pseudo)lines and boxes

We say a family of geometric objects C has (l;k)-property if every subfamily C0C of cardinality at most lisk- piercable. In this paper we investigate the existence of g(k;d)such that if any family of objects C in Rd has the (g(k;d);k)-property, then C is k-piercable. Danzer and Gr̈ unbaum showed that g(k;d)is infinite for fami-lies of boxes and translates of centrally symmetric convex hexagons. In this paper we show that any family of pseudo-lines(lines) with (k2+k+ 1;k)-property is k-piercable and extend this result to certain families of objects with discrete intersections. This is the first positive result for arbitrary k for a general family of objects. We also pose a relaxed ver-sion of the above question and show that any family of boxes in Rd with (k2d;k)-property is 2dk- piercable.

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.