Random lasso

We propose a computationally intensive method, the random lasso method, for variable selection in linear models. The method consists of two major steps. In step 1, the lasso method is applied to many bootstrap samples, each using a set of randomly selected covariates. A measure of importance is yielded from this step for each covariate. In step 2, a similar procedure to the first step is implemented with the exception that for each bootstrap sample, a subset of covariates is randomly selected with unequal selection probabilities determined by the covariates' importance. Adaptive lasso may be used in the second step with weights determined by the importance measures. The final set of covariates and their coefficients are determined by averaging bootstrap results obtained from step 2. The proposed method alleviates some of the limitations of lasso, elastic-net and related methods noted especially in the context of microarray data analysis: it tends to remove highly correlated variables altogether or select them all, and maintains maximal flexibility in estimating their coefficients, particularly with different signs; the number of selected variables is no longer limited by the sample size; and the resulting prediction accuracy is competitive or superior compared to the alternatives. We illustrate the proposed method by extensive simulation studies. The proposed method is also applied to a Glioblastoma microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS377 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Resting Place

This thesis describes the creation of an installation of art works where the artist wishes to convey feelings of rest and repose, of places where one can feel secure, comfortable, and free from fear. The medium for the installation pieces were thin stainless steel wires and paper, which were used to build nest and cocoon-like objects. These objects sprang from the artist\u27s need for protection from his environment

Radical-Enhanced Chinese Character Embedding

We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage. However, existing Chinese processing algorithms typically regard word or character as the basic unit but ignore the crucial radical information. In this paper, we fill this gap by leveraging radical for learning continuous representation of Chinese character. We develop a dedicated neural architecture to effectively learn character embedding and apply it on Chinese character similarity judgement and Chinese word segmentation. Experiment results show that our radical-enhanced method outperforms existing embedding learning algorithms on both tasks.Comment: 8 pages, 4 figure

A visibility graph approach to CNY exchange rate networks and characteristic analysis

We find that exchange rate networks are significantly similar from the perspective of topological structure, though with relatively great differences in fluctuation characteristics from perspective of exchange rate time series. First, we transform central parity rate time series of US dollar, Euro, Yen, and Sterling against CNY into exchange rate networks with visibility graph algorithm and find consistent topological characteristics in four exchange rate networks, with their average path lengths 5 and average clustering coefficients 0.7. Further, we reveal that all four transformed exchange rate networks show hierarchical structure and small-world and scale-free properties, with their hierarchy indexes 0.5 and power exponents 1.5. Both of the US dollar network and Sterling network exhibit assortative mixing features, while the Euro network and Yen network exhibit disassortative mixing features. Finally, we research community structure of exchange rate networks and uncover the fact that the communities are actually composed by large amounts of continuous time point fractions and small amounts of discrete time point fractions. In this way, we can observe that the spread of time series values corresponding to nodes inside communities is significantly lower than the spread of those values corresponding to nodes of the whole networks

Possible hard X-ray shortages in bursts from KS 1731-260 and 4U 1705-44

Aims: A hard X-ray shortage, implying the cooling of the corona, was observed during bursts of IGR J17473-272, 4U 1636-536, Aql X-1, and GS 1826-238. Apart from these four sources, we investigate here an atoll sample, in which the number of bursts for each source is larger than 5, to explore the possible additional hard X-ray shortage during {\it Rossi X-ray timing explorer (RXTE)} era. Methods: According to the source catalog that shows type-I bursts, we analyzed all the available pointing observations of these sources carried out by the {\it RXTE} proportional counter array (PCA). We grouped and combined the bursts according to their outburst states and searched for the possible hard X-ray shortage while bursting. Results: We found that the island states of KS 1731-260 and 4U 1705-44 show a hard X-ray shortage at significant levels of 4.5 and 4.7 $\sigma$ and a systematic time lag of $0.9 \pm 2.1$ s and $2.5 \pm 2.0$ s with respect to the soft X-rays, respectively. While in their banana branches and other sources, we did not find any consistent shortage.Comment: 5 pages, 4 figures, accepted by A&A as a research not