Di- and tri-metallic complexes containing two bridging cyclodiphosphazane ligands: the crystal structure of [MO2(CO)8{µ-cis-[PhNP(OC6H4Me-p)]2}2]

The reactions of the mononuclear cyclodiphosphazane complexes, cis-[Mo(CO)(4){cis-[PhNP(OR)](2)}(2)] with [Mo(CO)(4)(nbd)] (nbd = norbornadiene). [Mo(CO)(4)(NHC5H10)(2)] or [MCl(2)(cod)] (cod = cycloocta-1,5-diene) afforded the homobimetallic complexes; [Mo-2(CO)(8){mu-cis-[PhNP(OR)](2)}(2)] (R = C(5)H(4)Me-p 5 or CH2CF3 6) or the heterobimetallic complexes. [Mo-2(CO)(8){mu-cis-[PhNP(OE)](2)}(2)MCl(2)] (R = C(6)H(4)Me-p; M = Pd 7 or Pt 8). In all the above complexes, the two metal moieties are bridged by two cyclodiphosphazane ligands. The reactions of the mononuclear complexes, cis-[M(CO)(4)(A){cis-[PhNP(OC(6)H(4)Me-p)](2)}] with (M'Cl-2(cod)] afforded the trinuclear complexes, cis-[M'Cl-2[M(CO)(4)(A){cis-[PhNP(OC(6)H(4)Me-p)](2)}](2)] (M' = Pd, M = Mo, A = P(OMe)(3) 10; M' = Pt, M = Mo. A = P(OMe)(3) 11; M' = Pd. M = W. A = NHC5H10 12; M' = Pt, M = W. A = NHC5H10 13). The structure of the complex 5 has been determined by single-crystal X-ray crystallography.

Synthetic, spectroscopic and structural studies on uranium and thorium complexes of diphosphazane dioxides

Co-ordination complexes of the diphosphazane dioxides Ph(2)P(O)N(Pr-i)P(O)Ph(2) L(1). Ph(2)P(O)N(Pr-i)P(O)Ph(OC(6)H(4)Me-4) L(2) and Ph(2)P(O)N(Pr-i)P(O)(O2C12H8) L(3) with UO22+ or Th4+ ions have been synthesised and characterised by IR and NMR spectroscopy. The structures of [UO2(NO3)(2)L(1)] and [Th(NO3)(2)L(3)(1)][Th(NO3)(6)] are established by X-ray crystallography. In the former, the uranyl ion is bonded to two bidentate nitrate groups and the two phosphoryl groups of the ligand L(1); the co-ordination polyhedron around the metal is a hexagonal bipyramid. The cationic moiety in the thorium complex contains three bidentate diphosphazane dioxide ligands and two bidentate nitrate groups around the ten-co-ordinated metal.

Purification and characterization of DNA-dependent RNA polymerase II from Candida utilis

DNA-dependent RNA polymerase II from Candida utilis has been purified to near homogeneity. The purified enzyme resolved into three subforms, viz. IIO, IIA and IIB. On SDS-PAGE the enzyme showed ten polypeptides with molecular weights in the range of 205 kDa to 14 kDa. By two dimensional electrophoresis (IEF followed by SDS-PAGE) the presence of basic and acidic polypeptides has been demonstrated. The enzyme showed Km values of 5, 5.6 and 8 mu M for GTP, CTP and ATP, respectively, and the activity was inhibited by low levels of oc-amanitin and antibodies raised against bovine RNA polymerase II. By Western blot analysis the enzyme was found to cross-react with antibodies to bovine RNA polymerase II. RNA polymerase II from G. utilis is a phosphoprotein, the subunits RPB1 and RPB10 were found to be phosphorylated. Analysis of carboxy-terminal domain indicated that it was functionally redundant at least in case of nonspecific transcription, implicating its role in other nuclear processes, such as promoter specific initiation or transcription activation or RNA processing.

Space vector pulsewidth modulation — A status review

The technique of space vector pulsewidth modulation (SVM) is reviewed. The basic principle of SVM is derived and is compared with sine-triangle PWM. Operation in the overmodulation range is explained. Extension of SVM to other inverter-motor combinations such as three level inverters and split phase motors are discussed.

Resent radar observations of asteroid 1566 Icarus

We report Doppler-only radar observations of Icarus at Goldstone at a transmitter frequency of 8510 MHz (3.5 cm wavelength) during 8-10 June 1996, the first radar detection of the object since 1968. Optimally filtered and folded spectra achieve a maximum opposite-circular (OC) polarization signal-to-noise ratio of about 10 and help to constrain Icarus' physical properties. We obtain an OC radar cross section of 0.05 km(2) (with a 35% uncertainty), which is less than values estimated by Goldstein (1969) and by Pettengill et al. (1969), and a circular polarization (SC/OC) ratio of 0.5+/-0.2. We analyze the echo power spectrum with a model incorporating the echo bandwidth B and a spectral shape parameter it, yielding a coupled constraint between B and n. We adopt 25 Hz as the lower bound on B, which gives a lower bound on the maximum pole-on breadth of about 0.6 km and upper bounds on the radar and optical albedos that are consistent with Icarus' tentative QS classification. The observed circular polarization ratio indicates a very rough near-surface at spatial scales of the order of the radar wavelength. (C) 1999 Elsevier Science Ltd. All rights reserved.

Robust Formulations for Handling Uncertainty in Kernel Matrices

We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we derive a formulation which is robust to such noise. The resulting formulation applies when the noise is Gaussian, or has finite support. The formulation in general is non-convex, but in several cases of interest it reduces to a convex program. The problem of uncertainty in kernel matrix is motivated from the real world problem of classifying proteins when the structures are provided with some uncertainty. The formulation derived here naturally incorporates such uncertainty in a principled manner leading to significant improvements over the state of the art. 1.

Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions

In this paper we consider the problem of learning an n × n kernel matrix from m(1) similarity matrices under general convex loss. Past research have extensively studied the m = 1 case and have derived several algorithms which require sophisticated techniques like ACCP, SOCP, etc. The existing algorithms do not apply if one uses arbitrary losses and often can not handle m > 1 case. We present several provably convergent iterative algorithms, where each iteration requires either an SVM or a Multiple Kernel Learning (MKL) solver for m > 1 case. One of the major contributions of the paper is to extend the well knownMirror Descent(MD) framework to handle Cartesian product of psd matrices. This novel extension leads to an algorithm, called EMKL, which solves the problem in O(m2 log n 2) iterations; in each iteration one solves an MKL involving m kernels and m eigen-decomposition of n × n matrices. By suitably defining a restriction on the objective function, a faster version of EMKL is proposed, called REKL,which avoids the eigen-decomposition. An alternative to both EMKL and REKL is also suggested which requires only an SVMsolver. Experimental results on real world protein data set involving several similarity matrices illustrate the efficacy of the proposed algorithms.

Online character recognition using regression techniques

This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Landslide Susceptible Locations in Western Ghats: Prediction through openModeller

Many shallow landslides are triggered by heavy rainfall on hill slopes resulting in enormous casualties and huge economic losses in mountainous regions. Hill slope failure usually occurs as soil resistance deteriorates in the presence of the acting stress developed due to a number of reasons such as increased soil moisture content, change in land use causing slope instability, etc. Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration and information related to land surface susceptibility. Terrain analysis applications using spatial data such as aspect, slope, flow direction, compound topographic index, etc. along with information derived from remotely sensed data such as land cover / land use maps permit us to quantify and characterise the physical processes governing the landslide occurrence phenomenon. In this work, the probable landslide prone areas are predicted using two different algorithms – GARP (Genetic Algorithm for Rule-set Prediction) and Support Vector Machine (SVM) in a free and open source software package - openModeller. Several environmental layers such as aspect, digital elevation data, flow accumulation, flow direction, slope, land cover, compound topographic index, and precipitation data were used in modelling. A comparison of the simulated outputs, validated by overlaying the actual landslide occurrence points showed 92% accuracy with GARP and 96% accuracy with SVM in predicting landslide prone areas considering precipitation in the wettest month whereas 91% and 94% accuracy were obtained from GARP and SVM considering precipitation in the wettest quarter of the year.

A Randomized Algorithm for Large Scale Support Vector Learning

We propose a randomized algorithm for large scale SVM learning which solves the problem by iterating over random subsets of the data. Crucial to the algorithm for scalability is the size of the subsets chosen. In the context of text classification we show that, by using ideas from random projections, a sample size of O(log n) can be used to obtain a solution which is close to the optimal with a high probability. Experiments done on synthetic and real life data sets demonstrate that the algorithm scales up SVM learners, without loss in accuracy. 1

Multiclass Core Vector Machine

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we propose a Multiclass Core Vector Machine(MCVM). Here we formulate the multiclass SVM problem as a Quadratic Programming(QP) problem defining an SVM with vector valued output. This QP problem is then solved using the CVM technique to achieve scalability to handle large data sets. Experiments done with several large synthetic and real world data sets show that the proposed MCVM technique gives good generalization performance as that of SVM at a much lesser computational expense. Further, it is observed that MCVM scales well with the size of the data set.

Rough Core Vector Clustering

Support Vector Clustering has gained reasonable attention from the researchers in exploratory data analysis due to firm theoretical foundation in statistical learning theory. Hard Partitioning of the data set achieved by support vector clustering may not be acceptable in real world scenarios. Rough Support Vector Clustering is an extension of Support Vector Clustering to attain a soft partitioning of the data set. But the Quadratic Programming Problem involved in Rough Support Vector Clustering makes it computationally expensive to handle large datasets. In this paper, we propose Rough Core Vector Clustering algorithm which is a computationally efficient realization of Rough Support Vector Clustering. Here Rough Support Vector Clustering problem is formulated using an approximate Minimum Enclosing Ball problem and is solved using an approximate Minimum Enclosing Ball finding algorithm. Experiments done with several Large Multi class datasets such as Forest cover type, and other Multi class datasets taken from LIBSVM page shows that the proposed strategy is efficient, finds meaningful soft cluster abstractions which provide a superior generalization performance than the SVM classifier.

Hexa-arm star shaped hydrazone derivatives from hexakis(4-formylphenoxy)-cyclotriphosphazene core

A series of novel hexasubstituted cyclophosphazene hydrazones [N(3)P(3)(-OC(6)H(4)-p-CH=N-NH-C(O)-C(6)H(4)-p-X)(6)] (X = H, Br, Cl, F, OH, OCH(3), CH(3), NO(2), NH(2)) were prepared by a sixfold condensation reaction of [N(3)P(3)(-OC(6)H(4)-p-CHO)(6)] with para-substituted benzoic hydrazides [NH(2)-NH-C(O)-C(6)H(4)-p-X] with excellent yields (91-98%). The structures of the compounds were confirmed by elemental analysis, FT-IR, (1)H, (13)C, (31)P, 2D-HSQC NMR and mass spectrometry (MALDI-TOF). All the synthesized cyclophosphazene hydrazones exhibit high thermal stability. The crystal structure of a homogeneously substituted hexakis(4-formylphenoxy)-cyclotriphosphazene was determined by X-ray diffraction analysis. The compound crystallizes in the monoclinic system, space group P2(1)/n with a = 16.558(3) angstrom, b = 10.250(2) angstrom, c = 23.429(5) angstrom, alpha = gamma = 90.00 degrees, beta = 90.461(4)degrees, V = 3976.5(14) angstrom(3) and Z = 4. The R value is 0.0823 for 4290 observed reflections. The conformations of the 4-formylphenoxy-groups are different at the three phosphorus atoms. (C) 2011 Elsevier B.V. All rights reserved.

A homobimetallic complex of chromium(0) with a sigma-borane component

The synthesis, characterization, and reactivity of a chromium(0) complex bearing an amine-borane moiety (eta(6)-C(6)H(5)CH(2)NMe(2)center dot BH(3))Cr(CO)(3) (2) is reported. Photolysis of complex 2 results in the elimination of a CO ligand followed by the formation of an intramolecular sigma-borane complex (eta(1)-(eta(6)- C(6)H(5)CH(2)NMe(2)center dot BH(2)-H))Cr(CO)(2) (3). This species was characterized in solution by NMR spectroscopy. Reaction of complex 2 with photochemically generated (OC)(5)Cr(THF) affords a novel homobimetallic sigma-borane complex (OC)(3)Cr(eta(6)-C(6)H(5)CH(2)NMe(2)center dot BH(2)-H-Cr(CO)(5)) (4), wherein one of the BH moieties is bound to the chromium center in an eta(1)-fashion. The sigma-borane complex 4 was isolated in moderate to good yield (72%). The BH(3) fragment in the complexes 3 and 4 are highly dynamic involving exchange of the BH hydrogen bound to the metal with the terminal BH hydrogen atoms. The dynamics has been studied using variable-temperature NMR spectroscopy. Complexes 2 and 4 have been characterized by X-ray crystallography.