1,562 research outputs found

    Template epitaxial growth of thermoelectric Bi/BiSb superlattice nanowires by charge-controlled pulse electrodeposition

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    © The Electrochemical Society, Inc. 2009. All rights reserved. Except as provided under U.S. copyright law, this work may not be reproduced, resold, distributed, or modified without the express permission of The Electrochemical Society (ECS). The archival version of this work was published in The Journal of The Electrochemical Society, 156(9), 2009.Bi/BiSb superlattice nanowires (SLNWs) with a controllable and very small bilayer thickness and a sharp segment interface were grown by adopting a charge-controlled pulse electrodeposition. The deposition parameters were optimized to ensure an epitaxial growth of the SLNWs with a preferential orientation. The segment length and bilayer thickness of the SLNWs can be controlled simply by changing the modulating time, and the consistency of the segment length can be well maintained by our approach. The Bravais law in the electrodeposited nanowires is verified by the SLNW structure. The current–voltage measurement shows that the SLNWs have good electrical conductance, particularly those with a smaller bilayer thickness. The Bi/BiSb SLNWs might have excellent thermoelectric performances.National Natural Science Foundation of China and the National Major Project of Fundamental Research for Nanomaterials and Nanostructures

    Learning to locate relative outliers

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    Outliers usually spread across regions of low density. However, due to the absence or scarcity of outliers, designing a robust detector to sift outliers from a given dataset is still very challenging. In this paper, we consider to identify relative outliers from the target dataset with respect to another reference dataset of normal data. Particularly, we employ Maximum Mean Discrepancy (MMD) for matching the distribution between these two datasets and present a novel learning framework to learn a relative outlier detector. The learning task is formulated as a Mixed Integer Programming (MIP) problem, which is computationally hard. To this end, we propose an effective procedure to find a largely violated labeling vector for identifying relative outliers from abundant normal patterns, and its convergence is also presented. Then, a set of largely violated labeling vectors are combined by multiple kernel learning methods to robustly locate relative outliers. Comprehensive empirical studies on real-world datasets verify that our proposed relative outlier detection outperforms existing methods. © 2011 S. Li & I.W. Tsang

    Face Hallucination With Finishing Touches

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    Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both tasks seamlessly for daily-life unconstrained face images. In this paper, we present a novel Vivid Face Hallucination Generative Adversarial Network (VividGAN) for simultaneously super-resolving and frontalizing tiny non-frontal face images. VividGAN consists of coarse-level and fine-level Face Hallucination Networks (FHnet) and two discriminators, i.e., Coarse-D and Fine-D. The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i.e., fine-grained facial components, to attain a frontal HR face image with authentic details. In the fine-level FHnet, we also design a facial component-aware module that adopts the facial geometry guidance as clues to accurately align and merge the frontal coarse HR face and prior information. Meanwhile, two-level discriminators are designed to capture both the global outline of a face image as well as detailed facial characteristics. The Coarse-D enforces the coarsely hallucinated faces to be upright and complete while the Fine-D focuses on the fine hallucinated ones for sharper details. Extensive experiments demonstrate that our VividGAN achieves photo-realistic frontal HR faces, reaching superior performance in downstream tasks, i.e., face recognition and expression classification, compared with other state-of-the-art methods

    Broadband Reconfiguration of OptoMechanical Filters

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    We demonstrate broad-band reconfiguration of coupled photonic crystal nanobeam cavities by using optical gradient force induced mechanical actuation. Propagating waveguide modes that exist over wide wavelength range are used to actuate the structures and in that way control the resonance of localized cavity mode. Using this all-optical approach, more than 18 linewidths of tuning range is demonstrated. Using on-chip temperature self-referencing method that we developed, we determined that 20 % of the total tuning was due to optomechanical reconfiguration and the rest due to thermo-optic effects. Independent control of mechanical and optical resonances of our structures, by means of optical stiffening, is also demonstrated

    Investigation of Thermal Diffusivity for Nano-Phase Composite Ceramics

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    In recent years, there has been increasing interest in nanostructured ceramic materials because of their advanced properties. The superplasticity; improved strength and toughness, higher densification rate and low sintering temperature are observed in these materials[1–4]. The sintering speed vsintering of ceramic is related to the grain size d by the expression of vsin ting ∝d-4 [2]. By adding the nano-particles of Al2O3 to conventional Al2O3, the strength, fracture toughness and thermal resistance of Al2O3 ceramic can increase[5]. Up to now, only a few researchers[6,7] have concerned about the specific heat and thermal expansion of nano- ceramics, and no report about thermal diffusivity of nano-composite has been found in literature. However, characterizations of thermal diffusivity of nanocomposite ceramics are very important for designing and preparing this kind of new materials.</p

    Prognostic gene network modules in breast cancer hold promise

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    A substantial proportion of lymph node-negative patients who receive adjuvant chemotherapy do not derive any benefit from this aggressive and potentially toxic treatment. However, standard histopathological indices cannot reliably detect patients at low risk of relapse or distant metastasis. In the past few years several prognostic gene expression signatures have been developed and shown to potentially outperform histopathological factors in identifying low-risk patients in specific breast cancer subgroups with predictive values of around 90%, and therefore hold promise for clinical application. We envisage that further improvements and insights may come from integrative expression pathway analyses that dissect prognostic signatures into modules related to cancer hallmarks

    Structure and mechanism of human DNA polymerase η

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    The variant form of the human syndrome xeroderma pigmentosum (XPV) is caused by a deficiency in DNA polymerase eta (Pol eta), a DNA polymerase that enables replication through ultraviolet-induced pyrimidine dimers. Here we report high-resolution crystal structures of human Pol eta at four consecutive steps during DNA synthesis through cis-syn cyclobutane thymine dimers. Pol eta acts like a 'molecular splint' to stabilize damaged DNA in a normal B-form conformation. An enlarged active site accommodates the thymine dimer with excellent stereochemistry for two-metal ion catalysis. Two residues conserved among Pol eta orthologues form specific hydrogen bonds with the lesion and the incoming nucleotide to assist translesion synthesis. On the basis of the structures, eight Pol eta missense mutations causing XPV can be rationalized as undermining the molecular splint or perturbing the active-site alignment. The structures also provide an insight into the role of Pol eta in replicating through D loop and DNA fragile sites

    Factorization and resummation of s-channel single top quark production

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    In this paper we study the factorization and resummation of s-channel single top quark production in the Standard Model at both the Tevatron and the LHC. We show that the production cross section in the threshold limit can be factorized into a convolution of hard function, soft function and jet function via soft-collinear-effective-theory (SCET), and resummation can be performed using renormalization group equation in the momentum space resummation formalism. We find that in general, the resummation effects enhance the Next-to-Leading-Order (NLO) cross sections by about 33%-5% at both the Tevatron and the LHC, and significantly reduce the factorization scale dependence of the total cross section at the Tevatron, while at the LHC we find that the factorization scale dependence has not been improved, compared with the NLO results.Comment: 29 pages, 7 figures; version published in JHE
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