Highly efficient interfacing of guided plasmons and photons in nanowires

Successful exploitations of strongly confined surface plasmon-polaritons critically rely on their efficient and rapid conversion to lossless channels. We demonstrate a simple, robust, and broad-band butt-coupling technique for connecting a metallic nanowire and a dielectric nanofiber. Conversion efficiencies above 95% in the visible and close to 100% in the near infrared can be achieved with realistic parameters. Moreover, by combining butt-coupling with nanofocusing, we propose a broad-band high-throughput near-field optical microscope.Comment: 5 figure

Coherent interaction of a metallic structure with a single quantum emitter: from super absorption to cloaking

We provide a general theoretical platform based on quantized radiation in absorptive and inhomogeneous media for investigating the coherent interaction of light with metallic structures in the immediate vicinity of quantum emitters. In the case of a very small metallic cluster, we demonstrate extreme regimes where a single emitter can either counteract or enhance particle absorption by three orders of magnitude. For larger structures, we show that an emitter can eliminate both scattering and absorption and cloak a plasmonic antenna. We provide physical interpretations of our results and discuss their applications in active metamaterials and quantum plasmonics

A Single-Emitter Gain Medium for Bright Coherent Radiation from a Plasmonic Nanoresonator

We theoretically demonstrate the generation and radiation of coherent nanoplasmons powered by a single three-level quantum emitter on a plasmonic nanoresonator. By pumping the three-level emitter in a Raman configuration, we show a pathway to achieve macroscopic accumulation of nanoplasmons due to stimulated emission in the nanoresonator despite their fast relaxation. Thanks to the antenna effect of the nanoresonator, the system acts as an efficient and bright nanoscopic coherent light source with a photon emission rate of hundreds of Terahertz and could be realized with solid-state emitters at room temperatures in pulse mode. We provide physical interpretations of the results and discuss their realization and implications for ultra-compact integration of optoelectronics.Comment: 15 pages, 7 figure

Effect of Fractal Dimension of Fine Aggregates on the Concrete Chloride Resistance

The relationship between fractal dimension of fine aggregates and the chloride resistance of concrete was investigated in this study. Both concrete and mortar specimens were cast. Concrete specimens were in the same mix design as the mortar specimens except for the coarse aggregates. The specimens were divided into different groups based on the gradation of the fine aggregates. The chloride resistances of concrete specimens were tested by using the rapid chloride migration method. The results indicate that high volume fractal dimensions of fine aggregates have positive impacts on the chloride resistance of concrete. The simplified calculating formulas were proposed

Kernel-based distance metric learning for microarray data classification

BACKGROUND: The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample size, which make the task quite challenging. RESULTS: In this paper, we present a modified K-nearest-neighbor (KNN) scheme, which is based on learning an adaptive distance metric in the data space, for cancer classification using microarray data. The distance metric, derived from the procedure of a data-dependent kernel optimization, can substantially increase the class separability of the data and, consequently, lead to a significant improvement in the performance of the KNN classifier. Intensive experiments show that the performance of the proposed kernel-based KNN scheme is competitive to those of some sophisticated classifiers such as support vector machines (SVMs) and the uncorrelated linear discriminant analysis (ULDA) in classifying the gene expression data. CONCLUSION: A novel distance metric is developed and incorporated into the KNN scheme for cancer classification. This metric can substantially increase the class separability of the data in the feature space and, hence, lead to a significant improvement in the performance of the KNN classifier

bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies.

Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions shows that Markov Blanket-based methods are capable of finding genetic variants strongly associated with common diseases and reducing false positives when the number of instances is large. Unfortunately, a typical dataset from genome-wide association studies consists of very limited number of examples, where current methods including Markov Blanket-based method may perform poorly. RESULTS: To address small sample problems, we propose a Bayesian network-based approach (bNEAT) to detect epistatic interactions. The proposed method also employs a Branch-and-Bound technique for learning. We apply the proposed method to simulated datasets based on four disease models and a real dataset. Experimental results show that our method outperforms Markov Blanket-based methods and other commonly-used methods, especially when the number of samples is small. CONCLUSIONS: Our results show bNEAT can obtain a strong power regardless of the number of samples and is especially suitable for detecting epistatic interactions with slight or no marginal effects. The merits of the proposed approach lie in two aspects: a suitable score for Bayesian network structure learning that can reflect higher-order epistatic interactions and a heuristic Bayesian network structure learning method

Domain-Based Predictive Models for Protein-Protein Interaction Prediction

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well