Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix.

Spatially Adaptive Kernel Regression Using Risk Estimation

An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.

Photophysical, electrochemical and solid state properties of diketopyrrolopyrrole based molecular materials: importance of the donor group

Diketopyrrolopyrrole (DPP) based molecular semiconductors have emerged as promising materials for high performance active layers in organic solar cells. It is imperative to comprehend the origin of such a property by investigating the fundamental structure property correlation. In this report we have investigated the role of the donor group in DPP based donor-acceptor- donor (D-A-D) structure to govern the solid state, photophysical and electrochemical properties. We have prepared three derivatives of DPP with varying strengths of the donor groups, such as phenyl (PDPP-Hex), thiophene (TDPP-Hex) and selenophene (SeDPP-Hex). The influence of the donor units on the solid state packing was studied by single crystal X-ray diffraction. The photophysical, electrochemical and density functional theory ( DFT) results were combined to elucidate the structural and electronic properties of three DPP derivatives. We found that these DPP derivatives crystallized in the monoclinic space group P21/c and show herringbone packing in the crystal lattice. The derivatives exhibit weak p-p stacking interactions as two neighboring molecules slip away from each other with varied torsional angles at the donor units. The high torsional angle of 32 degrees ( PDPP-Hex) between the phenyl and lactam ring results in weak intramolecular interactions between the donor and acceptor, while TDPP-Hex and SeDPP-Hex show lower torsional angles of 9 degrees and 12 degrees with a strong overlap between the donor and acceptor units. The photophysical properties reveal that PDPP-Hex exhibits a high Stokes shift of 0.32 eV and SeDPP- Hex shows a high molar absorption co-efficient of 33 600 L mol -1 1 cm -1 1 with a low band gap of similar to 2.2 eV. The electrochemical studies of SeDPP- Hex indicate the pronounced effect of selenium in stabilizing the LUMO energy levels and this further emphasizes the importance of chalcogens in developing new n-type organic semiconductors for optoelectronic devices.

FeatureMatch: A General ANNF Estimation Technique and its Applications

In this paper, we propose FeatureMatch, a generalised approximate nearest-neighbour field (ANNF) computation framework, between a source and target image. The proposed algorithm can estimate ANNF maps between any image pairs, not necessarily related. This generalisation is achieved through appropriate spatial-range transforms. To compute ANNF maps, global colour adaptation is applied as a range transform on the source image. Image patches from the pair of images are approximated using low-dimensional features, which are used along with KD-tree to estimate the ANNF map. This ANNF map is further improved based on image coherency and spatial transforms. The proposed generalisation, enables us to handle a wider range of vision applications, which have not been tackled using the ANNF framework. We illustrate two such applications namely: 1) optic disk detection and 2) super resolution. The first application deals with medical imaging, where we locate optic disks in retinal images using a healthy optic disk image as common target image. The second application deals with super resolution of synthetic images using a common source image as dictionary. We make use of ANNF mappings in both these applications and show experimentally that our proposed approaches are faster and accurate, compared with the state-of-the-art techniques.

Forest area estimation and reporting: implications for conservation, management and REDD

Periodic estimation, monitoring and reporting on area under forest and plantation types and afforestation rates are critical to forest and biodiversity conservation, sustainable forest management and for meeting international commitments. This article is aimed at assessing the adequacy of the current monitoring and reporting approach adopted in India in the context of new challenges of conservation and reporting to international conventions and agencies. The analysis shows that the current mode of monitoring and reporting of forest area is inadequate to meet the national and international requirements. India could be potentially over-reporting the area under forests by including many non-forest tree categories such as commercial plantations of coconut, cashew, coffee and rubber, and fruit orchards. India may also be under-reporting deforestation by reporting only gross forest area at the state and national levels. There is a need for monitoring and reporting of forest cover, deforestation and afforestation rates according to categories such as (i) natural/primary forest, (ii) secondary/degraded forests, (iii) forest plantations, (iv) commercial plantations, (v) fruit orchards and (vi) scattered trees.

Biomimetic molecular organization of naphthalene diimide in the solid state: tunable (chiro-) optical, viscoelastic and nanoscale properties

The interfacing of aromatic molecules with biomolecules to design functional molecular materials is a promising area of research. Intermolecular interactions determine the performance of these materials and therefore, precise control over the molecular organization is necessary to improve functional properties. Herein we describe the tunable biomimetic molecular engineering of a promising n-type organic semiconductor, naphthalene diimide (NDI), in the solid state by introducing minute structural mutations in the form of amino acids with variable Ca-functionality. For the first time we could achieve all four possible crystal packing modes, namely cofacial, brickwork, herringbone and slipped stacks of the NDI system. Furthermore, amino acid conjugated NDIs exhibit ultrasonication induced organogels with tunable visco-elastic and temperature responsive emission properties. The amino acid-NDI conjugates self-assemble into 0D nanospheres and 1D nanofibers in their gel state while the ethylamine-NDI conjugate forms 2D sheets from its solution. Photophysical studies indicated the remarkable influence of molecular ordering on the absorption and fluorescence properties of NDIs. Interestingly, the circular dichroism (CD) and X-ray diffraction (XRD) studies revealed the existence of helical ordering of NDIs in both solution and solid state. The chiral amino acids and their conformations with respect to the central NDI core are found to influence the nature of the helical organization of NDIs. Consequently, the origin of the preferential handedness in the helical organization is attributed to transcription of chiral information from the amino acid to the NDI core. On account of these unique properties, the materials derived from NDI-conjugates might find a wide range of future interdisciplinary applications from materials to biomedicine.

Toward a Scalable Working Set Size Estimation Method and Its Application for Chip Multiprocessors

It is essential to accurately estimate the working set size (WSS) of an application for various optimizations such as to partition cache among virtual machines or reduce leakage power dissipated in an over-allocated cache by switching it OFF. However, the state-of-the-art heuristics such as average memory access latency (AMAL) or cache miss ratio (CMR) are poorly correlated to the WSS of an application due to 1) over-sized caches and 2) their dispersed nature. Past studies focus on estimating WSS of an application executing on a uniprocessor platform. Estimating the same for a chip multiprocessor (CMP) with a large dispersed cache is challenging due to the presence of concurrently executing threads/processes. Hence, we propose a scalable, highly accurate method to estimate WSS of an application. We call this method ``tagged WSS (TWSS)'' estimation method. We demonstrate the use of TWSS to switch-OFF the over-allocated cache ways in Static and Dynamic NonUniform Cache Architectures (SNUCA, DNUCA) on a tiled CMP. In our implementation of adaptable way SNUCA and DNUCA caches, decision of altering associativity is taken by each L2 controller. Hence, this approach scales better with the number of cores present on a CMP. It gives overall (geometric mean) 26% and 19% higher energy-delay product savings compared to AMAL and CMR heuristics on SNUCA, respectively.

Signal tracking approach for phase estimation in digital holographic interferometry

In this research work, we introduce a novel approach for phase estimation from noisy reconstructed interference fields in digital holographic interferometry using an unscented Kalman filter. Unlike conventionally used unwrapping algorithms and piecewise polynomial approximation approaches, this paper proposes, for the first time to the best of our knowledge, a signal tracking approach for phase estimation. The state space model derived in this approach is inspired from the Taylor series expansion of the phase function as the process model, and polar to Cartesian conversion as the measurement model. We have characterized our approach by simulations and validated the performance on experimental data (holograms) recorded under various practical conditions. Our study reveals that the proposed approach, when compared with various phase estimation methods available in the literature, outperforms at lower SNR values (i.e., especially in the range 0-20 dB). It is demonstrated with experimental data as well that the proposed approach is a better choice for estimating rapidly varying phase with high dynamic range and noise. (C) 2014 Optical Society of America

Smoothed Functional Algorithms for Stochastic Optimization Using q-Gaussian Distributions

Smoothed functional (SF) schemes for gradient estimation are known to be efficient in stochastic optimization algorithms, especially when the objective is to improve the performance of a stochastic system However, the performance of these methods depends on several parameters, such as the choice of a suitable smoothing kernel. Different kernels have been studied in the literature, which include Gaussian, Cauchy, and uniform distributions, among others. This article studies a new class of kernels based on the q-Gaussian distribution, which has gained popularity in statistical physics over the last decade. Though the importance of this family of distributions is attributed to its ability to generalize the Gaussian distribution, we observe that this class encompasses almost all existing smoothing kernels. This motivates us to study SF schemes for gradient estimation using the q-Gaussian distribution. Using the derived gradient estimates, we propose two-timescale algorithms for optimization of a stochastic objective function in a constrained setting with a projected gradient search approach. We prove the convergence of our algorithms to the set of stationary points of an associated ODE. We also demonstrate their performance numerically through simulations on a queuing model.

Estimation of voice-onset time in continuous speech using temporal measures

This paper proposes an automatic acoustic-phonetic method for estimating voice-onset time of stops. This method requires neither transcription of the utterance nor training of a classifier. It makes use of the plosion index for the automatic detection of burst onsets of stops. Having detected the burst onset, the onset of the voicing following the burst is detected using the epochal information and a temporal measure named the maximum weighted inner product. For validation, several experiments are carried out on the entire TIMIT database and two of the CMU Arctic corpora. The performance of the proposed method compares well with three state-of-the-art techniques. (C) 2014 Acoustical Society of America

Optimal Binary Power Control for Underlay CR With Different Interference Constraints and Impact of Channel Estimation Errors

Adapting the power of secondary users (SUs) while adhering to constraints on the interference caused to primary receivers (PRxs) is a critical issue in underlay cognitive radio (CR). This adaptation is driven by the interference and transmit power constraints imposed on the secondary transmitter (STx). Its performance also depends on the quality of channel state information (CSI) available at the STx of the links from the STx to the secondary receiver and to the PRxs. For a system in which an STx is subject to an average interference constraint or an interference outage probability constraint at each of the PRxs, we derive novel symbol error probability (SEP)-optimal, practically motivated binary transmit power control policies. As a reference, we also present the corresponding SEP-optimal continuous transmit power control policies for one PRx. We then analyze the robustness of the optimal policies when the STx knows noisy channel estimates of the links between the SU and the PRxs. Altogether, our work develops a holistic understanding of the critical role played by different transmit and interference constraints in driving power control in underlay CR and the impact of CSI on its performance.

OPTICAL FLOW ESTIMATION USING APPROXIMATE NEAREST NEIGHBOR FIELD FUSION

This paper proposes an optical flow algorithm by adapting Approximate Nearest Neighbor Fields (ANNF) to obtain a pixel level optical flow between image sequence. Patch similarity based coherency is performed to refine the ANNF maps. Further improvement in mapping between the two images are obtained by fusing bidirectional ANNF maps between pair of images. Thus a highly accurate pixel level flow is obtained between the pair of images. Using pyramidal cost optimization, the pixel level optical flow is further optimized to a sub-pixel level. The proposed approach is evaluated on the middlebury dataset and the performance obtained is comparable with the state of the art approaches. Furthermore, the proposed approach can be used to compute large displacement optical flow as evaluated using MPI Sintel dataset.

Electronic and Magnetic Properties of Yttrium-doped Silicon Carbide Nanotubes: Density Functional Theory Investigations

The electronic structure of yttrium-doped Silicon Carbide Nanotubes has been theoretically investigated using first principles density functional theory (DFT). Yttrium atom is bonded strongly on the surface of the nanotube with a binding energy of 2.37 eV and prefers to stay on the hollow site at a distance of around 2.25 angstrom from the tube. The semi-conducting nanotube with chirality (4, 4) becomes half mettalic with a magnetic moment of 1.0 mu(B) due to influence of Y atom on the surface. There is strong hybridization between d orbital of Y with p orbital of Si and C causing a charge transfer from d orbital of the Y atom to the tube. The Fermi level is shifted towards higher energy with finite Density of States for only upspin channel making the system half metallic and magnetic which may have application in spintronic devices.

Prediction of electronically nonadiabatic decomposition mechanisms of isolated gas phase nitrogen-rich energetic salt: Guanidium-triazolate

Electronically nonadiabatic decomposition pathways of guanidium triazolate are explored theoretically. Nonadiabatically coupled potential energy surfaces are explored at the complete active space self-consistent field (CASSCF) level of theory. For better estimation of energies complete active space second order perturbation theories (CASPT2 and CASMP2) are also employed. Density functional theory (DFT) with B3LYP functional and MP2 level of theory are used to explore subsequent ground state decomposition pathways. In comparison with all possible stable decomposition products (such as, N-2, NH3, HNC, HCN, NH2CN and CH3NC), only NH3 (with NH2CN) and N-2 are predicted to be energetically most accessible initial decomposition products. Furthermore, different conical intersections between the S-1 and S-0 surfaces, which are computed at the CASSCF(14,10)/6-31G(d) level of theory, are found to play an essential role in the excited state deactivation process of guanidium triazolate. This is the first report on the electronically nonadiabatic decomposition mechanisms of isolated guanidium triazolate salt. (C) 2015 Elsevier B.V. All rights reserved.

Origin of the Spin-Orbital Liquid State in a Nearly J=0 Iridate Ba3ZnIr2O9

We show using detailed magnetic and thermodynamic studies and theoretical calculations that the ground state of Ba3ZnIr2O9 is a realization of a novel spin-orbital liquid state. Our results reveal that Ba3ZnIr2O9 with Ir5+ (5d(4)) ions and strong spin-orbit coupling (SOC) arrives very close to the elusive J = 0 state but each Ir ion still possesses a weak moment. Ab initio density functional calculations indicate that this moment is developed due to superexchange, mediated by a strong intradimer hopping mechanism. While the Ir spins within the structural Ir2O9 dimer are expected to form a spin-orbit singlet state (SOS) with no resultant moment, substantial frustration arising from interdimer exchange interactions induce quantum fluctuations in these possible SOS states favoring a spin-orbital liquid phase down to at least 100 mK.