Depth functions as measures of representativeness

Data depth provides a natural means to rank multivariate vectors with respect to an underlying multivariate distribution. Most existing depth functions emphasize a centre-outward ordering of data points, which may not provide a useful geometric representation of certain distributional features, such as multimodality, of concern to some statistical applications. Such inadequacy motivates us to develop a device for ranking data points according to their “representativeness” rather than “centrality” with respect to an underlying distribution of interest. Derived essentially from a choice of goodness-of-fit test statistic, our device calls for a new interpretation of “depth” more akin to the concept of density than location. It copes particularly well with multivariate data exhibiting multimodality. In addition to providing depth values for individual data points, depth functions derived from goodness-of-fit tests also extend naturally to provide depth values for subsets of data points, a concept new to the data-depth literature.postprin

Control of plume interference effects on axisymmetric afterbodies

Plume interference effects on the axisymmetric flowfields around powered missiles are investigated using computational techniques. The study is mainly to understand the physics of the plume-induced shock and separation particularly at high plume to exit pressure ratios with and without shock-turbulent boundary layer control methods

Glueball Matrix Elements on Anisotropic Lattices

The glueball-to-vacuum matrix elements of local gluonic operators in scalar, tensor, and pseudoscalar channels are investigated numerically on several anisotropic lattices with the spatial lattice spacing in the range 0.1fm -- 0.2fm. These matrix elements are needed to predict the glueball branching ratios in $J/\psi$ radiative decays which will help to identify the glueball states in experiments. Two types of improved local gluonic operators are constructed for a self-consistent check, and the finite volume effects are also studied. The lattice spacing dependence of our results is very small and the continuum limits are reliably extrapolated.Comment: 3 pages, 3 figures, Lattice2003 (spectrum

Chiral magnetoresistance in Pt/Co/Pt zigzag wires

The Rashba effect leads to a chiral precession of the spins of moving electrons while the Dzyaloshinskii-Moriya interaction (DMI) generates preference towards a chiral profile of local spins. We predict that the exchange interaction between these two spin systems results in a 'chiral' magnetoresistance depending on the chirality of the local spin texture. We observe this magnetoresistance by measuring the domain wall (DW) resistance in a uniquely designed Pt/Co/Pt zigzag wire, and by changing the chirality of the DW with applying an in-plane magnetic field. A chirality-dependent DW resistance is found, and a quantitative analysis shows a good agreement with a theory based on the Rashba model. Moreover, the DW resistance measurement allows us to independently determine the strength of the Rashba effect and the DMI simultaneously, and the result implies a possible correlation between the Rashba effect, the DMI, and the symmetric Heisenberg exchange

Roper Resonance and S_{11}(1535) from Lattice QCD

Using the constrained curve fitting method and overlap fermions with the lowest pion mass at $180 {\rm MeV}$, we observe that the masses of the first positive and negative parity excited states of the nucleon tend to cross over as the quark masses are taken to the chiral limit. Both results at the physical pion mass agree with the experimental values of the Roper resonance ($N^{1/2+}(1440)$) and $S_{11}$ ($N^{1/2-}(1535)$). This is seen for the first time in a lattice QCD calculation. These results are obtained on a quenched Iwasaki $16^3 \times 28$ lattice with $a = 0.2 {\rm fm}$. We also extract the ghost $\eta' N$ states (a quenched artifact) which are shown to decouple from the nucleon interpolation field above $m_{\pi} \sim 300 {\rm MeV}$. From the quark mass dependence of these states in the chiral region, we conclude that spontaneously broken chiral symmetry dictates the dynamics of light quarks in the nucleon.Comment: 10 pages, 5 figures, revised version to appear in PL

Abscisic Acid Uridine Diphosphate Glucosyltransferases Play a Crucial Role in Abscisic Acid Homeostasis in Arabidopsis

The phytohormone abscisic acid (ABA) is crucial for plant growth and adaptive responses to various stress conditions. Plants continuously adjust the ABA level to meet physiological needs, but how ABA homeostasis occurs is not fully understood. This study provides evidence that UGT71B6, an ABA uridine diphosphate glucosyltransferase (UGT), and its two closely related homologs, UGT71B7 and UGT71B8, play crucial roles in ABA homeostasis and in adaptation to dehydration, osmotic stress, and high-salinity stresses in Arabidopsis (Arabidopsis thaliana). UGT RNA interference plants that had low levels of these three UGT transcripts displayed hypersensitivity to exogenous ABA and high-salt conditions during germination and exhibited a defect in plant growth. However, the ectopic expression of UGT71B6 in the atbg1 (for beta-glucosidase) mutant background aggravated the ABA-deficient phenotype of atbg1 mutant plants. In addition, modulation of the expression of the three UGTs affects the expression of CYP707A1 to CYP707A4, which encode ABA 8 '-hydroxylases; four CYP707As were expressed at higher levels in the UGT RNA interference plants but at lower levels in the UGT71B6:GFP-overexpressing plants. Based on these data, this study proposes that UGT71B6 and its two homologs play a critical role in ABA homeostasis by converting active ABA to an inactive form (abscisic acid-glucose ester) depending on intrinsic cellular and environmental conditions in plants.X113026Ysciescopu

On the discovery of continuous truth: a semi-supervised approach with partial ground truths

In many applications, the information regarding to the same object can be collected from multiple sources. However, these multi-source data are not reported consistently. In the light of this challenge, truth discovery is emerged to identify truth for each object from multi-source data. Most existing truth discovery methods assume that ground truths are completely unknown, and they focus on the exploration of unsupervised approaches to jointly estimate object truths and source reliabilities. However, in many real world applications, a set of ground truths could be partially available. In this paper, we propose a semi-supervised truth discovery framework to estimate continuous object truths. With the help of ground truths, even a small amount, the accuracy of truth discovery can be improved. We formulate the semi-supervised truth discovery problem as an optimization task where object truths and source reliabilities are modeled as variables. The ground truths are modeled as a regularization term and its contribution to the source weight estimation can be controlled by a parameter. The experiments show that the proposed method is more accurate and efficient than the existing truth discovery methods