A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

Novel algorithms for distributed sequential hypothesis testing

This paper considers sequential hypothesis testing in a decentralized framework. We start with two simple decentralized sequential hypothesis testing algorithms. One of which is later proved to be asymptotically Bayes optimal. We also consider composite versions of decentralized sequential hypothesis testing. A novel nonparametric version for decentralized sequential hypothesis testing using universal source coding theory is developed. Finally we design a simple decentralized multihypothesis sequential detection algorithm.

Cooperative distributed sequential spectrum sensing

We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.

A novel algorithm for cooperative distributed sequential spectrum sensing in Cognitive Radio

This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.

Cooperative distributed sequential spectrum sensing

We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.

Synthesis and phase evolution in Nb/Si multilayers obtained by sequential laser ablation

The paper reports the synthesis of Nb/Si multilayers (48/27 nm) deposited on Si single crystal substrate by sequential laser ablation of elemental Nb and Si. Significant amount of Nb is found in the amorphous Si layer (similar to 25-35 at.% Nb). The Nb layer is found to be polycrystalline. The phase evolution of the multilayer has been studied by annealing at 600 degrees C for various times and carrying out cross sectional electron microscopic studies. We report the formation of amorphous silicide layer at the Nb/Si interface followed by the formation of the NbSi2 phase in the Si layer. Further annealing leads to the nucleation of hexagonal Nb5Si3 grains in amorphous silicide layers at Nb/NbSi2 interfaces. These results are different from those reported for sputter deposited multilayer. (C) 2013 Elsevier B. V. All rights reserved.

Sequential alternating proximal method for scalable sparse structural SVMs

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.

Proactive control of sequential saccades in the human supplementary eye field

Our ability to regulate behavior based on past experience has thus far been examined using single movements. However, natural behavior typically involves a sequence of movements. Here, we examined the effect of previous trial type on the concurrent planning of sequential saccades using a unique paradigm. The task consisted of two trial types: no-shift trials, which implicitly encouraged the concurrent preparation of the second saccade in a subsequent trial; and target-shift trials, which implicitly discouraged the same in the next trial. Using the intersaccadic interval as an index of concurrent planning, we found evidence for context-based preparation of sequential saccades. We also used functional MRI-guided, single-pulse, transcranial magnetic stimulation on human subjects to test the role of the supplementary eye field (SEF) in the proactive control of sequential eye movements. Results showed that (i) stimulating the SEF in the previous trial disrupted the previous trial type-based preparation of the second saccade in the nonstimulated current trial, (ii) stimulating the SEF in the current trial rectified the disruptive effect caused by stimulation in the previous trial, and (iii) stimulating the SEF facilitated the preparation of second saccades based on previous trial type even when the previous trial was not stimulated. Taken together, we show how the human SEF is causally involved in proactive preparation of sequential saccades.

Inversion of moments to retrieve joint probabilities in quantum sequential measurements

A sequence of moments obtained from statistical trials encodes a classical probability distribution. However, it is well known that an incompatible set of moments arises in the quantum scenario, when correlation outcomes associated with measurements on spatially separated entangled states are considered. This feature, viz., the incompatibility of moments with a joint probability distribution, is reflected in the violation of Bell inequalities. Here, we focus on sequential measurements on a single quantum system and investigate if moments and joint probabilities are compatible with each other. By considering sequential measurement of a dichotomic dynamical observable at three different time intervals, we explicitly demonstrate that the moments and the probabilities are inconsistent with each other. Experimental results using a nuclear magnetic resonance system are reported here to corroborate these theoretical observations, viz., the incompatibility of the three-time joint probabilities with those extracted from the moment sequence when sequential measurements on a single-qubit system are considered.

Nonparametric distributed sequential detection via universal source coding

We consider nonparametric or universal sequential hypothesis testing when the distribution under the null hypothesis is fully known but the alternate hypothesis corresponds to some other unknown distribution. These algorithms are primarily motivated from spectrum sensing in Cognitive Radios and intruder detection in wireless sensor networks. We use easily implementable universal lossless source codes to propose simple algorithms for such a setup. The algorithms are first proposed for discrete alphabet. Their performance and asymptotic properties are studied theoretically. Later these are extended to continuous alphabets. Their performance with two well known universal source codes, Lempel-Ziv code and KT-estimator with Arithmetic Encoder are compared. These algorithms are also compared with the tests using various other nonparametric estimators. Finally a decentralized version utilizing spatial diversity is also proposed and analysed.

Unprecedented formation of a 14-membered dihydropyran macrocycle via sequential olefin cross metathesis-intramolecular hetero Diels-Alder reaction

Formation of a 2,3-dihydro-4H-pyran containing 14-membered macrocycle by sequential olefin cross metathesis and a highly regiospecific hetero Diels-Alder reaction was observed in the reaction of a hydroxydienone derived from tartaric acid with Grubbs' second generation catalyst. It was found that the free alcohol in the hydroxyenone led to the macrocycle formation, while protection of the hydroxy group formed the ring closing metathesis product. (C) 2013 Elsevier Ltd. All rights reserved.

Segmental relaxations and crystallization-induced phase separation in PVDF/PMMA blends in the presence of surface-functionalized multiwall carbon nanotubes

Crystallization-induced phase separation and segmental relaxations in poly(vinylidene fluoride)/poly(methyl methacrylate) (PVDF/PMMA) blends was systematically investigated by melt-rheology and broadband dielectric spectroscopy in the presence of multiwall carbon nanotubes (MWNTs). Different functionalized MWNTs (amine, -NH2; acid, -COOH) were incorporated in the blends by melt-mixing above the melting temperature of PVDF, where the blends are miscible, and the crystallization induced phase separation was probed in situ by shear rheology. Interestingly, only -NH2 functionalized MWNTs (a-MWNTs) aided in the formation of beta-phase (trans-trans) crystals in PVDF, whereas both the neat blends and the blends with -COOH functionalized MWNTs (c-MWNTs) showed only alpha-phase (trans-gauche-trans-gauche') crystals as inferred from wide-angle X-ray diffraction (WXRD) and Fourier transform infrared (FTIR). Furthermore, blends with only a-MWNTs facilitated in heterogeneous nucleation in the blends manifesting in an increase in the calorimetric crystallization temperature and hence, augmented the theologically determined crystallintion induced phase separation temperature. The dielectric relaxations associated with the crystalline phase of PVDF (alpha(c)) was completely absent in the blends with a-MWNTs in contrast to neat blends and the blends with c-MWNTs in the dielectric loss spectra. The relaxations in the blends investigated here appeared to follow Havriliak-Negami (HN) empirical equations, and, more interestingly, the dynamic heterogeneity in the system could be mapped by an extra relaxation at higher frequency at the crystallization-induced phase separation temperature. The mean relaxation time (tau(HN)) was evaluated and observed to be delayed in the presence of MWNTs in the blends, more prominently in the case of blends with a-MWNTs. The latter also showed a significant increase in the dielectric relaxation strength (Delta epsilon). Electron microscopy and selective etching was used to confirm the localization of MWNTs in the amorphous phases of the interspherulitic regions as observed from scanning electron microscopy (SEM). The evolved crystalline morphology, during crystallization-induced phase separation, was observed to have a strong influence on the charge transport processes in the blends. These observations were further supported by the specific interactions (like dipole induced dipole interaction) between a-MWNTs and PVDF, as inferred from FTIR, and the differences in the crystalline morphology as observed from WXRD and polarized optical microscopy (POM).

Temperature dependence of resistance and crystallization in amorphous Si15Te85-xGex thin films

Amorphous thin chalcogenide Si15Te85-xGex films (x: 5, 9, 10, 11, 12) are prepared by flash evaporation and the temperature dependence of resistance of these films has been studied in the temperature range 25-250 degrees C. All the compositions show a linear variation of resistance in this temperature range. Apart from the linear variation, a sharp reduction in resistance at one or at two distinct temperatures (T-TR1/T-TR2) is seen. Thin films annealed at these temperatures, when subjected to X-ray diffraction studies suggest that the dominant crystalline phase at T-TR1 and at T-TR2 is the same and the two dips are associated with varying levels of crystallization. This is also reflected in the atomic force microscopic (AFM) study. Further, the resistance of these two phases shows no drift when the films are annealed for varying lengths of time (10 min to 120 min) suggesting the stability of the phases.

Reversible shear-induced crystallization above equilibrium freezing temperature in a lyotropic surfactant system

We demonstrate a unique shear-induced crystallization phenomenon above the equilibrium freezing temperature (T-K(o)) in weakly swollen isotropic (L-i) and lamellar (L-alpha) mesophases with bilayers formed in a cationic-anionic mixed surfactant system. Synchrotron rheological X-ray diffraction study reveals the crystallization transition to be reversible under shear (i.e., on stopping the shear, the nonequilibrium crystalline phase L-c melts back to the equilibrium mesophase). This is different from the shear-driven crystallization below T-K(o), which is irreversible. Rheological optical observations show that the growth of the crystalline phase occurs through a preordering of the L-i phase to an L-alpha phase induced by shear flow, before the nucleation of the Lc phase. Shear diagram of the L-i phase constructed in the parameter space of shear rate ((gamma)) over dot vs. temperature exhibits L-i -> L-c and L-i -> L-alpha transitions above the equilibrium crystallization temperature (T-K(o)), in addition to the irreversible shear-driven nucleation of L-c in the L-i phase below T-K(o). In addition to revealing a unique class of nonequilibrium phase transition, the present study urges a unique approach toward understanding shear-induced phenomena in concentrated mesophases of mixed amphiphilic systems.

Electrical switching in amorphous Si-Te-Ge thin films: Impact of input energy on crystallization process and switching parameters

Electrical switching studies on amorphous Si15Te74Ge11 thin film devices show interesting changes in the switching behavior with changes in the input energy supplied; the input energy determines the extent of crystallization in the active volume, which is reflected in the value of SET resistances. This in turn, determines the trend exhibited by switching voltage (V-t) for different input conditions. The results obtained are analyzed on the basis of the amount of Joule heat generated, which determines the temperature of the active volume. Depending on the final temperature, devices are rendered either in the intermediate state with a resistance of 5*10(2) Omega or the ON state with a resistance of 5*10(1) Omega. (C) 2013 Elsevier B.V. All rights reserved.