The omega DD vertex in a sum rule approach

The study of charmonium dissociation in heavy ion collisions is generally performed in the framework of effective Lagrangians with meson exchange. Some studies are also developed with the intention of calculate form factors and coupling constants related with charmed and light mesons. These quantifies are important in the evaluation of charmonium cross sections. In this Letter we present a calculation of the omega DD vertex that is a possible interaction vertex in some meson-exchange models spread in the literature. We used the standard method of QCD sum rules in order to obtain the vertex form factor as a function of the transferred momentum. Our results are compatible with the value of this vertex form factor (at zero momentum transfer) obtained in the vector-meson dominance model. (c) 2006 Elsevier B.V. All rights reserved.

Bs0 meson and the Bs0BK coupling from QCD sum rules

We evaluate the mass of the B(s0) scalar meson and the coupling constant in the B(s0)BK vertex in the framework of QCD sum rules. We consider the B(s0) as a tetraquark state to evaluate its mass. We get m(Bs0) = (5.85 +/- 0.13) GeV, which is in agreement, considering the uncertainties, with predictions supposing it as a b (s) over bar state or a B (K) over bar bound state with J(P) = 0(+). To evaluate the g(Bs0BK) coupling, we use the three-point correlation functions of the vertex, considering B(s0) as a normal b (s) over bar state. The obtained coupling constant is: g(Bs0BK) = (16.3 +/- 3.2) GeV. This number is in agreement with light-cone QCD sum rules calculation. We have also compared the decay width of the B(s0) -> BK process considering the B(s0) to be a b (s) over bar state and a BK molecular state. The width obtained for the BK molecular state is twice as big as the width obtained for the b (s) over bar state. Therefore, we conclude that with the knowledge of the mass and the decay width of the B(s0) meson, one can discriminate between the different theoretical proposals for its structure.

Skewness, splitting number and vertex deletion of some toroidal meshes

The skewness sk(G) of a graph G = (V, E) is the smallest integer sk(G) >= 0 such that a planar graph can be obtained from G by the removal of sk(C) edges. The splitting number sp(G) of C is the smallest integer sp(G) >= 0 such that a planar graph can be obtained from G by sp(G) vertex splitting operations. The vertex deletion vd(G) of G is the smallest integer vd(G) >= 0 such that a planar graph can be obtained from G by the removal of vd(G) vertices. Regular toroidal meshes are popular topologies for the connection networks of SIMD parallel machines. The best known of these meshes is the rectangular toroidal mesh C(m) x C(n) for which is known the skewness, the splitting number and the vertex deletion. In this work we consider two related families: a triangulation Tc(m) x c(n) of C(m) x C(n) in the torus, and an hexagonal mesh Hc(m) x c(n), the dual of Tc(m) x c(n) in the torus. It is established that sp(Tc(m) x c(n)) = vd(Tc(m) x c(n) = sk(Hc(m) x c(n)) = sp(Hc(m) x c(n)) = vd(Hc(m) x c(n)) = min{m, n} and that sk(Tc(m) x c(n)) = 2 min {m, n}.

Exact solution of the A(n-1)((1)) trigonometric vertex model with non-diagonal open boundaries

The A(n-1)((1)) trigonometric vertex model with generic non-diagonal boundaries is studied. The double-row transfer matrix of the model is diagonalized by algebraic Bethe ansatz method in terms of the intertwiner and the corresponding face-vertex relation. The eigenvalues and the corresponding Bethe ansatz equations are obtained.

Level-one highest weight representations of U-q[g1(1 vertical bar 1)] and associated vertex operators

We study the level-one irreducible highest weight representations of U-q[gl(1\1)] and associated q-vertex operators. We obtain the exchange relations satisfied by these vertex operators. The characters and supercharacters associated with these irreducible representations are calculated'. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

U-q[(sl(2)over-cap vertical bar 1)] vertex operators, screen currents, and correlation functions at an arbitrary level

Bosonized q-vertex operators related to the four-dimensional evaluation modules of the quantum affine superalgebra U-q[sl((2) over cap\1)] are constructed for arbitrary level k=alpha, where alpha not equal 0,-1 is a complex parameter appearing in the four-dimensional evaluation representations. They are intertwiners among the level-alpha highest weight Fock-Wakimoto modules. Screen currents which commute with the action of U-q[sl((2) over cap/1)] up to total differences are presented. Integral formulas for N-point functions of type I and type II q-vertex operators are proposed. (C) 2000 American Institute of Physics. [S0022-2488(00)00608-3].

Non-diagonal solutions of the reflection equation for the trigonometric A((1)) (n-1) vertex model

We obtain a class of non-diagonal solutions of the reflection equation for the trigonometric A(n-1)((1)) vertex model. The solutions can be expressed in terms of intertwinner matrix and its inverse, which intertwine two trigonometric R-matrices. In addition to a discrete (positive integer) parameter l, 1 less than or equal to l less than or equal to n, the solution contains n + 2 continuous boundary parameters.

Vertex component analysis: a fast algorithm to extract endmembers spectra from hyperspectral data

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Vertex component analysis: a fast algorithm to unmix hyperspectral data

Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.

Parallel implementation of vertex component analysis for hyperspectral endmember extraction

International Conference with Peer Review 2012 IEEE International Conference in Geoscience and Remote Sensing Symposium (IGARSS), 22-27 July 2012, Munich, Germany

Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis ( VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

vertex effect in polycrystalline materials : simulation, a macroscopic model, and structural application

Polycrystal Plasticity, Yield-Vertex, Corner, Vertex-Effect, Microscale, Macroscale, Multiaxial, Torsional Buckling, Cruciform Column

RTOG 0525: Exploratory Subset Analysis from a Randomized Phase III Trial Comparing Standard (std) Adjuvant Temozolomide (TMZ) with a Dose-dense (dd) Schedule for Glioblastoma (GBM)

Purpose/Objective(s): RTwith TMZ is the standard for GBM. dd TMZ causes prolongedMGMTdepletion in mononuclear cells and possibly in tumor. The RTOG 0525 trial (ASCO 2011) did not show an advantage from dd TMZ for survival or progression free survival. We conducted exploratory, hypothesis-generating subset analyses to detect possible benefit from dd TMZ.Materials/Methods: Patients were randomized to std (150-200 mg/m2 x 5 d) or dd TMZ (75-100 mg/m2 x 21 d) q 4 weeks for 6- 12 cycles. Eligibility included age.18, KPS$ 60, and. 1 cm2 tissue for prospective MGMTanalysis for stratification. Furtheranalyses were performed for all randomized patients (''intent-to-treat'', ITT), and for all patients starting protocol therapy (SPT). Subset analyses were performed by RPA class (III, IV, V), KPS (90-100, = 50,\50), resection (partial, total), gender (female, male), and neurologic dysfunction (nf = none, minor, moderate).Results: No significant difference was seen for median OS (16.6 vs. 14.9 months), or PFS (5.5 vs. 6.7 months, p = 0.06). MGMT methylation was linked to improved OS (21.2 vs. 14 months, p\0.0001), and PFS (8.7 vs. 5.7 months, p\0.0001). For the ITT (n = 833), there was no OS benefit from dd TMZ in any subset. Two subsets showed a PFS benefit for dd TMZ: RPA class III (6.2 vs. 12.6 months, HR 0.69, p = 0.03) and nf = minor (HR 0.77, p = 0.01). For RPA III, dd dramatically delayed progression, but post-progression dd patients died more quickly than std. A similar pattern for nf = minor was observed. For the SPT group (n = 714) there was neither PFS nor OS benefit for dd TMZ, overall. For RPA class III and nf = minor, there was a PFS benefit for dd TMZ (HR 0.73, p = 0.08; HR 0.77, p = 0.02). For nf = moderate subset, both ITT and SPT, the std arm showed superior OS (14.4 vs. 10.9 months) compared to dd, without improved PFS (HR 1.46, p = 0.03; and HR 1.74, p = 0.01. In terms of methylation status within this subset, there were more methylated patients in the std arm of the ITT subset (n = 159; 32 vs. 24%). For the SPT subset (n = 124), methylation status was similar between arms.Conclusions: This study did not demonstrate improved OS for dd TMZ for any subgroup, but for 2 highly functional subgroups, PFS was significantly increased. These data generate the testable hypothesis that intensive treatment may selectively improve disease control in those most likely able to tolerate dd therapy. Interpretation of this should be considered carefully due to small sample size, the process of multiple observations, and other confounders.Acknowledgment: This project was supported by RTOG grant U10 CA21661, and CCOP grant U10 CA37422 from the National Cancer Institute (NCI).

Origenis Opera omnia ex variis editionibus et codicibus manu exaratis gallicanis, italicis, germanicis et anglicis collecta : atque adnotationibus illustrata, cum copiosis indicibus, vita auctoris et multis dissertationibus. T1 / opera et studio DD. Caroli et Caroli Vincentii Delarue... ; accurante J.-P. Migne...

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