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The nanostructures found in nature sometimes have elaborate, three-dimensional structures that consist of soft and flexible constituents, and which exhibit diverse mechanical and optical functions. Here, we introduce a facile, low-cost and scalable nanofabrication approach based on a hot embossing process that can replicate sub-micron to nanoscale features on elastomeric substrates. We have further developed this technique to achieve polymer/metal heterostructure nanopillar arrays via conformal coating of Au films on polymeric templates. Each nanopillar displays a smooth surface and a constant diameter along the vertical direction. Raman spectroscopy studies revealed that the metallic nanostructures decorated with methylene blue exhibited a dominant Raman peak at 1624 cm −1 that was enhanced more than 3000 times and seven times relative to bare planar Si and Au-coated planar polystyrene substrates, respectively. These results indicate that our nanopillar array can be exploited as a flexible, large area platform for surface-enhanced Raman spectroscopy. [ABSTRACT FROM AUTHOR]

In this paper, a hypergraph-based image segmentation framework is formulated in a supervised manner for many high-level computer vision tasks. To consider short- and long-range dependency among various regions of an image and also to incorporate wider selection of features, a higher-order correlation clustering (HO-CC) is incorporated in the framework. Correlation clustering (CC), which is a graph-partitioning algorithm, was recently shown to be effective in a number of applications such as natural language processing, document clustering, and image segmentation. It derives its partitioning result from a pairwise graph by optimizing a global objective function such that it simultaneously maximizes both intra-cluster similarity and inter-cluster dissimilarity. In the HO-CC, the pairwise graph which is used in the CC is generalized to a hypergraph which can alleviate local boundary ambiguities that can occur in the CC. Fast inference is possible by linear programming relaxation, and effective parameter learning by structured support vector machine is also possible by incorporating a decomposable structured loss function. Experimental results on various data sets show that the proposed HO-CC outperforms other state-of-the-art image segmentation algorithms. The HO-CC framework is therefore an efficient and flexible image segmentation framework. [ABSTRACT FROM AUTHOR]

Zn-polar (0001) surfaces are more chemically reactive than other surfaces of ZnO crystals and drive preferential anisotropic and asymmetric growth along the [0001] direction, which facilitates growth of c-axis oriented, one-dimensional ZnO nanostructures. Accordingly, capping the top (0001) surface of ZnO crystals can impede c-axis growth and thus serve to modulate growth habits. In this study, we generated vertically aligned ZnO hexagonal nanotube-rod (h- NTR) hybrids by modulating growth habits during a second-stage process. Electron microscopy studies revealed the formation of very thin (10-20 nm) single-crystalline nanotube walls along the edges of underlying hexagonal rod tops capped with Si. In addition, spatially resolved investigation of ZnO h- NTR indicated an abrupt increase in the measured bandgap across rod-tube junctions, which was ascribed to a quantum confinement effect and Burstein-Moss effect of carriers within the very thin nanotube walls. [ABSTRACT FROM AUTHOR]

Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number. The proposed method partitions the image by means of correlation clustering, maximizing a linear discriminant function defined over a superpixel graph. The parameters of the discriminant function that define task-specific similarity/dissimilarity among superpixels are estimated based on structured support vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better generalization ability while the construction of the superpixel graph used to define the discriminant function allows a rich set of features to be incorporated to improve discriminability and robustness. We evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that task-aware partitioning leads to better labeling performance than the partitioning computed by the state-of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image partitioning paradigm is widely applicable to improving performance in high-level image understanding tasks. [ABSTRACT FROM PUBLISHER]

Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types. [ABSTRACT FROM AUTHOR]

Nanoscale time-dependent mechanical-electrical coupled behavior of single crystal ZnO nanorods was systematically explored, which is essential for accessing the long-term reliability of the ZnO nanorod-based flexible devices. A series of compression creep tests combined with in-situ electrical measurement was performed on vertically-grown single crystal ZnO nanorods. Continuous measurement of the current (I)-voltage (V) curves before, during, after the creep tests revealed that I is non-negligibly increased as a result of the time-dependent deformation. Analysis of the I-V curves based on the thermionic emission-diffusion theory allowed extraction of nanorod resistance, which was shown to decrease as time-dependent deformation. Finally, based on the observations in this study, a simple analytical model for predicting the reduction in nanorod resistance as a function of creep strain that is induced from diffusional mechanisms is proposed, and this model was demonstrated to be in an excellent agreement with the experimental results. [ABSTRACT FROM AUTHOR]