Reconstruction of Two-Dimensional Spectra from One-Dimensional Projections-Examples from Solid State NMR

The use of Projection Reconstruction (PR) to obtain two-dimensional (2D) spectra from one-dimensional (1D) data in the solid state is illustrated. The method exploits multiple 1D spectra obtained using magic angle spinning and off-magic angle spinning. The spectra recorded under the influence of scaled heteronuclear scalar and dipolar couplings in the presence of homonuclear dipolar decoupling sequences have been used to reconstruct J/D Resolved 2D-NMR spectra. The use of just two 1D spectra is observed sufficient to reconstruct a J-resolved 2D-spectrum while a Separated Local Field (SLF) 2D-NMR spectrum could be obtained from three 1D spectra. The experimental techniques for recording the 10 spectra and procedure of reconstruction are discussed and the reconstructed results are compared with 20 experiments recorded in traditional methods. The application of the technique has been made to a solid polycrystalline sample and to a uniaxially oriented liquid crystal. Implementation of PR-NMR in solid state provides high-resolution spectra as well as leads to significant reduction in experimental time. The experiments are relatively simple and are devoid of several technical complications involved in performing the 2D experiments.

Tailoring the interface of an immiscible polymer blend by a mutually miscible homopolymer grafted onto graphene oxide: outstanding mechanical properties

A unique strategy was adopted here to improve the compatibility between the components of an immiscible polymer blend and strengthen the interface. PMMA, a mutually miscible polymer to both PVDF and ABS, improved the compatibility between the phases by localizing at the blends interface. This was supported by the core-shell formation with PMMA as the shell and ABS as the core as observed from the SEM micrographs. This phenomenon was strongly contingent on the concentration of PMMA in the blends. This strategy was further extended to localize graphene oxide (GO) sheets at the blends interface by chemically coupling it to PMMA (PMMA-g-GO). A dramatic increment of ca. 84% in the Young's modulus and ca. 124% in the yield strength was observed in the presence of PMMA-g-GO with respect to the neat blends. A simultaneous increment in both the strength and the modulus was observed in the presence of PMMA-g-GO whereas, only addition of GO resulted in a moderate improvement in the yield strength. This study reveals that a mutually miscible polymer can render compatibility between the immiscible pair and can improve the stress transfer at the interface.

A unique strategy towards high dielectric constant and low loss with multiwall carbon nanotubes anchored onto graphene oxide sheets

Multiwall carbon nanotubes (MWNTs) were anchored onto graphene oxide sheets (GOs) via diazonium and C-C coupling reactions and characterized by spectroscopic and electron microscopic techniques. The thus synthesized MWNT-GO hybrid was then melt mixed with 50/50 polyamide6-maleic anhydride-modified acrylonitrile-butadiene-styrene (PA6-mABS) blend to design materials with high dielectric constant (30) and low dielectric loss. The phase morphology was studied by SEM and it was observed that the MWNT-GO hybrid was selectively localized in the PA6 phase of the blend. The 30 scales with the concentration of MWNT-GO in the blends, which interestingly showed a very low dielectric loss (< 0.2) making them potential candidate for capacitors. In addition, the dynamic storage modulus scales with the fraction of MWNT-GO in the blends, demonstrating their reinforcing capability as well.

Optimal power allocation for protective jamming in wireless networks: A flow based model

We address the problem of passive eavesdroppers in multi-hop wireless networks using the technique of friendly jamming. The network is assumed to employ Decode and Forward (DF) relaying. Assuming the availability of perfect channel state information (CSI) of legitimate nodes and eavesdroppers, we consider a scheduling and power allocation (PA) problem for a multiple-source multiple-sink scenario so that eavesdroppers are jammed, and source-destination throughput targets are met while minimizing the overall transmitted power. We propose activation sets (AS-es) for scheduling, and formulate an optimization problem for PA. Several methods for finding AS-es are discussed and compared. We present an approximate linear program for the original nonlinear, non-convex PA optimization problem, and argue that under certain conditions, both the formulations produce identical results. In the absence of eavesdroppers' CSI, we utilize the notion of Vulnerability Region (VR), and formulate an optimization problem with the objective of minimizing the VR. Our results show that the proposed solution can achieve power-efficient operation while defeating eavesdroppers and achieving desired source-destination throughputs simultaneously. (C) 2015 Elsevier B.V. All rights reserved.

A Polymatroid Approach to Separable Convex Optimization with Linear Ascending Constraints

We revisit a problem studied by Padakandla and Sundaresan SIAM J. Optim., August 2009] on the minimization of a separable convex function subject to linear ascending constraints. The problem arises as the core optimization in several resource allocation problems in wireless communication settings. It is also a special case of an optimization of a separable convex function over the bases of a specially structured polymatroid. We give an alternative proof of the correctness of the algorithm of Padakandla and Sundaresan. In the process we relax some of their restrictions placed on the objective function.

Construction of Singer Subgroup Orbit Codes Based on Cyclic Difference Sets

A recent approach for the construction of constant dimension subspace codes, designed for error correction in random networks, is to consider the codes as orbits of suitable subgroups of the general linear group. In particular, a cyclic orbit code is the orbit of a cyclic subgroup. Hence a possible method to construct large cyclic orbit codes with a given minimum subspace distance is to select a subspace such that the orbit of the Singer subgroup satisfies the distance constraint. In this paper we propose a method where some basic properties of difference sets are employed to select such a subspace, thereby providing a systematic way of constructing cyclic orbit codes with specified parameters. We also present an explicit example of such a construction.

Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory

We propose a new approach to clustering. Our idea is to map cluster formation to coalition formation in cooperative games, and to use the Shapley value of the patterns to identify clusters and cluster representatives. We show that the underlying game is convex and this leads to an efficient biobjective clustering algorithm that we call BiGC. The algorithm yields high-quality clustering with respect to average point-to-center distance (potential) as well as average intracluster point-to-point distance (scatter). We demonstrate the superiority of BiGC over state-of-the-art clustering algorithms (including the center based and the multiobjective techniques) through a detailed experimentation using standard cluster validity criteria on several benchmark data sets. We also show that BiGC satisfies key clustering properties such as order independence, scale invariance, and richness.

Engineering Nanostructures by Decorating Magnetic Nanoparticles onto Graphene Oxide Sheets to Shield Electromagnetic Radiations

In this study, a minimum, reflection loss of 70 a was achieved, for a 6 mm thick shield (at 17.1 GHz frequency) employing a unique approach. This was accomplished by engineering nanostructures through decoration of magnetic nanopartides (nickel, Ni) onto graphene oxide (GO) sheets. Enhanced electromagnetic (EM) shielding was derived by selectively, localizing the nanoscopic particles in a specific phase of polyethylene (PE)/poly(ethylene oxide) (PEO) blends. By introduction of a conducting inclusion (like multiwall carbon nanotubes, MWNTs) together with the engineered nanostructures (nickel-decorated GO, (GO-Ni), the shielding efficiency can be enhanced significantly in contrast to physically mixing the particles in the blends. For instance, the composites showed a shielding efficiency >25 dB for a combination of MWNTS (3 wt %) and Ni nanoparticles (52 wt %) in PE/PEO blends. However, similar shielding effectiveness could be achieved for a combination of MWNTs (3 wt %) and 10 vol % of GO-Ni where in the effective concentration of Ni was only 19 wt %. The GO-Ni sheets facilitated in an efficient charge transfer as manifested from high electrical conductivity in the blends besides enhancing the permeability in the blends. It is envisioned that GO is simultaneously reduced in the process of synthesizing GO-Ni, and this facilitated in efficient charge transfer between the neighboring CNTs. More interestingly, the blends With MWNTs/GO-Ni attenuated the incoming EM radiation mostly by absorption. This study opens new avenues in designing polyolefin-based lightweight shielding materials by engineering nanostructures for numerous applications.

Minimization Problems Based on Relative alpha-Entropy II: Reverse Projection

In part I of this two-part work, certain minimization problems based on a parametric family of relative entropies (denoted I-alpha) were studied. Such minimizers were called forward I-alpha-projections. Here, a complementary class of minimization problems leading to the so-called reverse I-alpha-projections are studied. Reverse I-alpha-projections, particularly on log-convex or power-law families, are of interest in robust estimation problems (alpha > 1) and in constrained compression settings (alpha < 1). Orthogonality of the power-law family with an associated linear family is first established and is then exploited to turn a reverse I-alpha-projection into a forward I-alpha-projection. The transformed problem is a simpler quasi-convex minimization subject to linear constraints.

On Locally Gabriel Geometric Graphs

Let be a set of points in the plane. A geometric graph on is said to be locally Gabriel if for every edge in , the Euclidean disk with the segment joining and as diameter does not contain any points of that are neighbors of or in . A locally Gabriel graph(LGG) is a generalization of Gabriel graph and is motivated by applications in wireless networks. Unlike a Gabriel graph, there is no unique LGG on a given point set since no edge in a LGG is necessarily included or excluded. Thus the edge set of the graph can be customized to optimize certain network parameters depending on the application. The unit distance graph(UDG), introduced by Erdos, is also a LGG. In this paper, we show the following combinatorial bounds on edge complexity and independent sets of LGG: (i) For any , there exists LGG with edges. This improves upon the previous best bound of . (ii) For various subclasses of convex point sets, we show tight linear bounds on the maximum edge complexity of LGG. (iii) For any LGG on any point set, there exists an independent set of size .

An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints

We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating l(p) - l(2) projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an l(p)-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

Unique Covering Problems with Geometric Sets

The Exact Cover problem takes a universe U of n elements, a family F of m subsets of U and a positive integer k, and decides whether there exists a subfamily(set cover) F' of size at most k such that each element is covered by exactly one set. The Unique Cover problem also takes the same input and decides whether there is a subfamily F' subset of F such that at least k of the elements F' covers are covered uniquely(by exactly one set). Both these problems are known to be NP-complete. In the parameterized setting, when parameterized by k, Exact Cover is W1]-hard. While Unique Cover is FPT under the same parameter, it is known to not admit a polynomial kernel under standard complexity-theoretic assumptions. In this paper, we investigate these two problems under the assumption that every set satisfies a given geometric property Pi. Specifically, we consider the universe to be a set of n points in a real space R-d, d being a positive integer. When d = 2 we consider the problem when. requires all sets to be unit squares or lines. When d > 2, we consider the problem where. requires all sets to be hyperplanes in R-d. These special versions of the problems are also known to be NP-complete. When parameterizing by k, the Unique Cover problem has a polynomial size kernel for all the above geometric versions. The Exact Cover problem turns out to be W1]-hard for squares, but FPT for lines and hyperplanes. Further, we also consider the Unique Set Cover problem, which takes the same input and decides whether there is a set cover which covers at least k elements uniquely. To the best of our knowledge, this is a new problem, and we show that it is NP-complete (even for the case of lines). In fact, the problem turns out to be W1]-hard in the abstract setting, when parameterized by k. However, when we restrict ourselves to the lines and hyperplanes versions, we obtain FPT algorithms.

DETERMINISTIC ALGORITHMS FOR MATCHING AND PACKING PROBLEMS BASED ON REPRESENTATIVE SETS

In this work, we study the well-known r-DIMENSIONAL k-MATCHING ((r, k)-DM), and r-SET k-PACKING ((r, k)-SP) problems. Given a universe U := U-1 ... U-r and an r-uniform family F subset of U-1 x ... x U-r, the (r, k)-DM problem asks if F admits a collection of k mutually disjoint sets. Given a universe U and an r-uniform family F subset of 2(U), the (r, k)-SP problem asks if F admits a collection of k mutually disjoint sets. We employ techniques based on dynamic programming and representative families. This leads to a deterministic algorithm with running time O(2.851((r-1)k) .vertical bar F vertical bar. n log(2)n . logW) for the weighted version of (r, k)-DM, where W is the maximum weight in the input, and a deterministic algorithm with running time O(2.851((r-0.5501)k).vertical bar F vertical bar.n log(2) n . logW) for the weighted version of (r, k)-SP. Thus, we significantly improve the previous best known deterministic running times for (r, k)-DM and (r, k)-SP and the previous best known running times for their weighted versions. We rely on structural properties of (r, k)-DM and (r, k)-SP to develop algorithms that are faster than those that can be obtained by a standard use of representative sets. Incorporating the principles of iterative expansion, we obtain a better algorithm for (3, k)-DM, running in time O(2.004(3k).vertical bar F vertical bar . n log(2)n). We believe that this algorithm demonstrates an interesting application of representative families in conjunction with more traditional techniques. Furthermore, we present kernels of size O(e(r)r(k-1)(r) logW) for the weighted versions of (r, k)-DM and (r, k)-SP, improving the previous best known kernels of size O(r!r(k-1)(r) logW) for these problems.

Asymptotic behavior and interpretation of virtual states: The effects of confinement and of basis sets

A real-space high order finite difference method is used to analyze the effect of spherical domain size on the Hartree-Fock (and density functional theory) virtual eigenstates. We show the domain size dependence of both positive and negative virtual eigenvalues of the Hartree-Fock equations for small molecules. We demonstrate that positive states behave like a particle in spherical well and show how they approach zero. For the negative eigenstates, we show that large domains are needed to get the correct eigenvalues. We compare our results to those of Gaussian basis sets and draw some conclusions for real-space, basis-sets, and plane-waves calculations. (C) 2016 AIP Publishing LLC.