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.

An automatic algorithm for mixed mode crack growth rate based on drop potential method

A new automatic algorithm for the assessment of mixed mode crack growth rate characteristics is presented based on the concept of an equivalent crack. The residual ligament size approach is introduced to implementation this algorithm for identifying the crack tip position on a curved path with respect to the drop potential signal. The automatic algorithm accounting for the curvilinear crack trajectory and employing an electrical potential difference was calibrated with respect to the optical measurements for the growing crack under cyclic mixed mode loading conditions. The effectiveness of the proposed algorithm is confirmed by fatigue tests performed on ST3 steel compact tension-shear specimens in the full range of mode mixities from pure mode Ito pure mode II. (C) 2015 Elsevier Ltd. All rights reserved.

Hybrid Working Set Algorithm for SVM Learning With a Kernel Coprocessor on FPGA

Support vector machines (SVM) are a popular class of supervised models in machine learning. The associated compute intensive learning algorithm limits their use in real-time applications. This paper presents a fully scalable architecture of a coprocessor, which can compute multiple rows of the kernel matrix in parallel. Further, we propose an extended variant of the popular decomposition technique, sequential minimal optimization, which we call hybrid working set (HWS) algorithm, to effectively utilize the benefits of cached kernel columns and the parallel computational power of the coprocessor. The coprocessor is implemented on Xilinx Virtex 7 field-programmable gate array-based VC707 board and achieves a speedup of upto 25x for kernel computation over single threaded computation on Intel Core i5. An application speedup of upto 15x over software implementation of LIBSVM and speedup of upto 23x over SVMLight is achieved using the HWS algorithm in unison with the coprocessor. The reduction in the number of iterations and sensitivity of the optimization time to variation in cache size using the HWS algorithm are also shown.

Hydrogelation of bile acid-peptide conjugates and in situ synthesis of silver and gold nanoparticles in the hydrogel matrix

Fabricating supramolecular hydrogels with embedded metal nanostructures is important for the design of novel hybrid nanocomposite materials for diverse applications such as biosensing and chemosensing platforms, catalytic and antibacterial functional materials etc. Supramolecular self-assembly of bile acid-dipeptide conjugates has led to the formation of new supramolecular hydrogels. Gelation of these molecules depends strongly on the hydrophobic character of the bile acids. The possibility of in situ fabrication of Ag and Au NPs in these supramolecular hydrogels by incorporating Ag+ and Au3+ salts was investigated via photoreduction. Chemical reductions of Ag+ and Au3+ salts in the hydrogels were performed without adding any external stabilizing agents. In this report we have shown that the color, size and shape of silver nanoparticles formed by photoreduction depend on the amino acid residue of the side chain.

A fast algorithm for speech polarity detection using long-term linear prediction

Speech polarity detection is a crucial first step in many speech processing techniques. In this paper, an algorithm is proposed that improvises the existing technique using the skewness of the voice source (VS) signal. Here, the integrated linear prediction residual (ILPR) is used as the VS estimate, which is obtained using linear prediction on long-term frames of the low-pass filtered speech signal. This excludes the unvoiced regions from analysis and also reduces the computation. Further, a modified skewness measure is proposed for decision, which also considers the magnitude of the skewness of the ILPR along with its sign. With the detection error rate (DER) as the performance metric, the algorithm is tested on 8 large databases and its performance (DER=0.20%) is found to be comparable to that of the best technique (DER=0.06%) on both clean and noisy speech. Further, the proposed method is found to be ten times faster than the best technique.

Single-step, size-controlled synthesis of colloidal silver nanoparticles stabilized by octadecylamine

A single step process for the synthesis of size-controlled silver nanoparticles has been developed using a bifunctional molecule, octadecylamine (ODA). Octadecylamine complexes to Ag+ ions electrostatically, reduce them, and subsequently stabilizes the nanoparticles thus formed. Hence, octadecylamine simultaneously functions as both a reducing and a stabilizing agent. The amine-capped nanoparticles can be obtained in the form of dry powder, which is readily redispersible in aqueous and organic solvents. The particle size, and the nucleation and growth kinetics of silver nanoparticles could be tuned by varying the molar ratio of ODA to AgNO3. The UV-vis spectra of nanoparticles prepared with different concentrations of ODA displayed the well-defined plasmon band with maximum absorption around 425 nm. The formation of silver metallic nanoparticles was confirmed by their XRD pattern. The binding of ODA molecule on the surface of silver has been studied by FT-IR and NMR spectroscopy. The formation of well-dispersed spherical Ag nanoparticles has been confirmed by TEM analysis. The particle size and distribution are found to be dependent on the molar concentration of the amine molecule. Open aperture z-scans have been performed to measure the nonlinearity of Ag nanoparticles. (C) 2015 Published by Elsevier B.V.

Light Induced Metastable State of Silver Nitroprusside Probed by Raman Spectroscopy

Low temperature Raman spectroscopic measurements on silver nitroprusside (AgNP), Ag-2Fe(CN)(5)NO] powders display reversible features of a partially converted metastable state. The results are compared with similarly observed metastable state in case of sodium nitroprusside (NaNP) and the differences have been discussed in terms of possible resistance to metastable state formation offered by silver atoms on the basis of hard soft acid base (HSAB) theory.

Prior image based temporally constrained reconstruction algorithm for magnetic resonance guided high intensity focused ultrasound

Purpose: A prior image based temporally constrained reconstruction ( PITCR) algorithm was developed for obtaining accurate temperature maps having better volume coverage, and spatial, and temporal resolution than other algorithms for highly undersampled data in magnetic resonance (MR) thermometry. Methods: The proposed PITCR approach is an algorithm that gives weight to the prior image and performs accurate reconstruction in a dynamic imaging environment. The PITCR method is compared with the temporally constrained reconstruction (TCR) algorithm using pork muscle data. Results: The PITCR method provides superior performance compared to the TCR approach with highly undersampled data. The proposed approach is computationally expensive compared to the TCR approach, but this could be overcome by the advantage of reconstructing with fewer measurements. In the case of reconstruction of temperature maps from 16% of fully sampled data, the PITCR approach was 1.57x slower compared to the TCR approach, while the root mean square error using PITCR is 0.784 compared to 2.815 with the TCR scheme. Conclusions: The PITCR approach is able to perform more accurate reconstructions of temperature maps compared to the TCR approach with highly undersampled data in MR guided high intensity focused ultrasound. (C) 2015 American Association of Physicists in Medicine.

Outstanding dielectric constant and piezoelectric coefficient in electrospun nanofiber mats of PVDF containing silver decorated multiwall carbon nanotubes: assessing through piezoresponse force microscopy

In order to enhance the piezoelectric b-phase, PVDF was electrospun from DMF solution. The enhanced b-phase was discerned by comparing the electrospun fibers against the melt mixed samples. While both the processes resulted in phase transformation of a-to electroactive b-polymorph in PVDF, the fraction of b-phase was strongly dependent on the adopted process. Two different nanoscopic particles: carboxyl functionalized multiwall carbon nanotubes (CNTs) and silver (Ag) decorated CNTs were used to further enhance the piezoelectric coefficient in the electrospun fibers. Fourier transform infrared spectroscopy (FTIR) and wide-angle X-ray diffraction (XRD) supports the development of piezoelectric b-phase in PVDF. It was concluded that electrospinning was the best technique for inducing the b-polymorph in PVDF. This was attributed to the high voltage electrostatic field that generates extensional forces on the polymer chains that aligns the dipoles in one direction. The ferroelectric and piezoelectric measurement on electrospun fibers were studied using piezo-response force microscope (PFM). The Ag-CNTs filled PVDF electrospun fibers showed the highest piezoelectric coefficient (d(33) = 54 pm V-1) in contrast to PVDF/CNT fibers (35 pm V-1) and neat PVDF (30 pm V-1). This study demonstrates that the piezoelectric coefficient can be enhanced significantly by electrospinning PVDF containing Ag decorated nanoparticles.

Efficient density matrix renormalization group algorithm to study Y junctions with integer and half-integer spin

An efficient density matrix renormalization group (DMRG) algorithm is presented and applied to Y junctions, systems with three arms of n sites that meet at a central site. The accuracy is comparable to DMRG of chains. As in chains, new sites are always bonded to the most recently added sites and the superblock Hamiltonian contains only new or once renormalized operators. Junctions of up to N = 3n + 1 approximate to 500 sites are studied with antiferromagnetic (AF) Heisenberg exchange J between nearest-neighbor spins S or electron transfer t between nearest neighbors in half-filled Hubbard models. Exchange or electron transfer is exclusively between sites in two sublattices with N-A not equal N-B. The ground state (GS) and spin densities rho(r) = < S-r(z)> at site r are quite different for junctions with S = 1/2, 1, 3/2, and 2. The GS has finite total spin S-G = 2S(S) for even (odd) N and for M-G = S-G in the S-G spin manifold, rho(r) > 0(< 0) at sites of the larger (smaller) sublattice. S = 1/2 junctions have delocalized states and decreasing spin densities with increasing N. S = 1 junctions have four localized S-z = 1/2 states at the end of each arm and centered on the junction, consistent with localized states in S = 1 chains with finite Haldane gap. The GS of S = 3/2 or 2 junctions of up to 500 spins is a spin density wave with increased amplitude at the ends of arms or near the junction. Quantum fluctuations completely suppress AF order in S = 1/2 or 1 junctions, as well as in half-filled Hubbard junctions, but reduce rather than suppress AF order in S = 3/2 or 2 junctions.

Enhanced nonlinear optical properties of graphene oxide-silver nanocomposites measured by Z-scan technique

Nonlinear optical properties (NLO) of a graphene oxide-silver (GO-Ag) nanocomposite have been investigated by the Z-scan setup at Q-switched Nd:YAG laser second harmonic radiation i.e., at 532 nm excitation in a nanosecond regime. A noteworthy enhancement in the NLO properties in the GO-Ag nanocomposite has been reported in comparison with those of the synthesized GO nanosheet. The extracted value of third order nonlinear susceptibility (chi(3)), at a peak intensity of I-0 = 0.2 GW cm(-2), for GO-Ag has been found to be 2.8 times larger than that of GO. The enhancement in NLO properties in the GO-Ag nanocomposite may be attributed to the complex energy band structures formed during the synthesis which promote resonant transition to the conduction band via surface plasmon resonance (SPR) at low laser intensities and excited state transition (ESA) to the conduction band of GO at higher intensities. Along with this photogenerated charge carriers in the conduction band of silver or the increase in defect states during the formation of the GO-Ag nanocomposite may contribute to ESA. Open aperture Z-scan measurement indicates reverse saturable absorption (RSA) behavior of the synthesized nanocomposite which is a clear indication of the optical limiting (OL) ability of the nanocomposite.

Thermal co-reduction approach to vary size of silver nanoparticle: its microbial and cellular toxicology

In recent years, silver nanoparticles (AgNPs) have attracted considerable interest in the field of food, agriculture and pharmaceuticals mainly due to its antibacterial activity. AgNPs have also been reported to possess toxic behavior. The toxicological behavior of nanomaterials largely depends on its size and shape which ultimately depend on synthetic protocol. A systematic and detailed analysis for size variation of AgNP by thermal co-reduction approach and its efficacy toward microbial and cellular toxicological behavior is presented here. With the focus to explore the size-dependent toxicological variation, two different-sized NPs have been synthesized, i.e., 60 nm (Ag60) and 85 nm (Ag85). A detailed microbial toxicological evaluation has been performed by analyzing minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), diameter of inhibition zone (DIZ), growth kinetics (GrK), and death kinetics (DeK). Comparative cytotoxicological behavior was analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. It has been concluded by this study that the size of AgNPs can be varied, by varying the concentration of reactants and temperature called as ``thermal co-reduction'' approach, which is one of the suitable approaches to meet the same. Also, the smaller AgNP has shown more microbial and cellular toxicity.

Characterization of silver-kaolinite (AgK): an adsorbent for long-lived I-129 species

Bentonite is a preferred buffer and backfill material for deep geological disposal of high-level nuclear waste (HLW). Bentonite does not retain anions by virtue of its negatively charged basal surface. Imparting anion retention ability to bentonite is important to enable the expansive clay to retain long-lived I-129 (iodine-129; half-life = 16 million years) species that may escape from the HLW geological repository. Silver-kaolinite (AgK) material is prepared as an additive to improve the iodide retention capacity of bentonite. The AgK is prepared by heating kaolinite-silver nitrate mix at 400 degrees C to study the kaolinite influence on the transition metal ion when reacting at its dehydroxylation temperature. Thermo gravimetric-Evolved Gas Detection analysis, X-ray diffraction analysis, X-ray photo electron spectroscopy and electron probe micro analysis indicated that silver occurs as AgO/Ag2O surface coating on thermally reacting kaolinite with silver nitrate at 400 degrees C.

l(1)-K-SVD: A robust dictionary learning algorithm with simultaneous update

We develop a new dictionary learning algorithm called the l(1)-K-svp, by minimizing the l(1) distortion on the data term. The proposed formulation corresponds to maximum a posteriori estimation assuming a Laplacian prior on the coefficient matrix and additive noise, and is, in general, robust to non-Gaussian noise. The l(1) distortion is minimized by employing the iteratively reweighted least-squares algorithm. The dictionary atoms and the corresponding sparse coefficients are simultaneously estimated in the dictionary update step. Experimental results show that l(1)-K-SVD results in noise-robustness, faster convergence, and higher atom recovery rate than the method of optimal directions, K-SVD, and the robust dictionary learning algorithm (RDL), in Gaussian as well as non-Gaussian noise. For a fixed value of sparsity, number of dictionary atoms, and data dimension, l(1)-K-SVD outperforms K-SVD and RDL on small training sets. We also consider the generalized l(p), 0 < p < 1, data metric to tackle heavy-tailed/impulsive noise. In an image denoising application, l(1)-K-SVD was found to result in higher peak signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise. The structural similarity index increases by 0.1 for low input PSNR, which is significant and demonstrates the efficacy of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

A Stochastic Approximation Algorithm for Quantile Estimation

In this paper, we present two new stochastic approximation algorithms for the problem of quantile estimation. The algorithms uses the characterization of the quantile provided in terms of an optimization problem in 1]. The algorithms take the shape of a stochastic gradient descent which minimizes the optimization problem. Asymptotic convergence of the algorithms to the true quantile is proven using the ODE method. The theoretical results are also supplemented through empirical evidence. The algorithms are shown to provide significant improvement in terms of memory requirement and accuracy.