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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]

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]

Biomaterials-based nanovaccines, such as those made of poly(lactic-co-glycolic acid) (PLGA), can induce stronger immunity than soluble antigens in healthy wild-type mouse models. However, whether metabolic syndrome can influence the immunological responses of nanovaccines remains poorly understood. Here, we first show that alteration in the sensing of the gut microbiome through Toll-like receptor 5 (TLR5) and the resulting metabolic syndrome in TLR5 -/- mice diminish the germinal center immune response induced by PLGA nanovaccines. The PLGA nanovaccines, unexpectedly, further changed gut microbiota. By chronically treating mice with antibiotics, we show that disrupting gut microbiome leads to poor vaccine response in an obesity-independent manner. We next demonstrate that the low immune response can be rescued by an immunomodulatory Pyr-pHEMA nanogel vaccine, which functions through TLR2 stimulation, enhanced trafficking, and induced stronger germinal center response than alum-supplemented PLGA nanovaccines. The study highlights the potential for immunomodulation under gut-mediated metabolic syndrome conditions using advanced nanomaterials.

Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.