Multilabel Consensus Classification

In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to counteract the effects of low data quality and the bias of any single model or data source, and thus can improve the robustness and the performance of predictive models. Out of privacy, storage and bandwidth considerations, in certain circumstances one has to combine the predictions from multiple models or data sources to obtain the final predictions without accessing the raw data. Consensus-based prediction combination algorithms are effective for such situations. However, current research on prediction combination focuses on the single label setting, where an instance can have one and only one label. Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time. Direct applications of existing prediction combination methods to multilabel settings can lead to degenerated performance. In this paper, we address the challenges of combining predictions from multiple multilabel classifiers and propose two novel algorithms, MLCM-r (MultiLabel Consensus Maximization for ranking) and MLCM-a (MLCM for microAUC). These algorithms can capture label correlations that are common in multilabel classifications, and optimize corresponding performance metrics. Experimental results on popular multilabel classification tasks verify the theoretical analysis and effectiveness of the proposed methods

Testing for pure-jump processes for high-frequency data

Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the validity of using pure-jump models. However, these tests suffer from several drawbacks, such as requiring rather stringent conditions and having slow rates of convergence. In this paper, we propose a different test to check whether the underlying process of high-frequency data can be modeled by a pure-jump process. The new test is based on the realized characteristic function, and enjoys a much faster convergence rate of order $O(n^{1/2})$ (where $n$ is the sample size) versus the usual $o(n^{1/4})$ available for existing tests; it is applicable much more generally than previous tests; for example, it is robust to jumps of infinite variation and flexible modeling of the diffusion component. Simulation studies justify our findings and the test is also applied to some real high-frequency financial data.Comment: Published at http://dx.doi.org/10.1214/14-AOS1298 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Characterisation of dispersions within annealed HVOLF thermally sprayed AlSnCu coatings

High velocity oxy-liquid fuel (HVOLF) AlSnCu coatings are characterised following annealing for up to 5 hours at 300°C. A combination of statistical analysis of BSE images and TEM observations demonstrate the decrease in the number of sub-micron and nanoscale Sn particles with annealing, commensurate with a decrease in the coating microhardness. TEM evidence further suggests the coarsening of nanoscale Sn through a mechanism of a liquid phase migration within the Al matrix. EELS and EFTEM additionally allow the identification of the precipitation of theta'

Quenching depends on morphologies: implications from the ultraviolet-optical radial color distributions in Green Valley Galaxies

In this Letter, we analyse the radial UV-optical color distributions in a sample of low redshift green valley (GV) galaxies, with the Galaxy Evolution Explorer (GALEX)+Sloan Digital Sky Survey (SDSS) images, to investigate how the residual recent star formation distribute in these galaxies. We find that the dust-corrected $u-r$ colors of early-type galaxies (ETGs) are flat out to $R_{90}$, while the colors turn blue monotonously when $r>0.5R_{50}$ for late-type galaxies (LTGs). More than a half of the ETGs are blue-cored and have remarkable positive NUV$-r$ color gradients, suggesting that their star formation are centrally concentrated; the rest have flat color distributions out to $R_{90}$. The centrally concentrated star formation activity in a large portion of ETGs is confirmed by the SDSS spectroscopy, showing that $\sim$50 % ETGs have EW(H$\rm \alpha$)$>6.0$ \AA. For the LTGs, 95% of them show uniform radial color profiles, which can be interpreted as a red bulge plus an extended blue disk. The links between the two kinds of ETGs, e.g., those objects having remarkable "blue-cored" and those having flat color gradients, are less known and require future investigations. It is suggested that the LTGs follow a general picture that quenching first occur in the core regions, and then finally extend to the rest of the galaxy. Our results can be re-examined and have important implications for the IFU surveys, such as MaNGA and SAMI.Comment: ApJ Letter, accepted. Five figure

Valley-selective optical Stark effect in monolayer WS2

Breaking space-time symmetries in two-dimensional crystals (2D) can dramatically influence their macroscopic electronic properties. Monolayer transition-metal dichalcogenides (TMDs) are prime examples where the intrinsically broken crystal inversion symmetry permits the generation of valley-selective electron populations, even though the two valleys are energetically degenerate, locked by time-reversal symmetry. Lifting the valley degeneracy in these materials is of great interest because it would allow for valley-specific band engineering and offer additional control in valleytronic applications. While applying a magnetic field should in principle accomplish this task, experiments to date have observed no valley-selective energy level shifts in fields accessible in the laboratory. Here we show the first direct evidence of lifted valley degeneracy in the monolayer TMD WS2. By applying intense circularly polarized light, which breaks time-reversal symmetry, we demonstrate that the exciton level in each valley can be selectively tuned by as much as 18 meV via the optical Stark effect. These results offer a novel way to control valley degree of freedom, and may provide a means to realize new valley-selective Floquet topological phases in 2D TMDs