Learning Spatial-Aware Regressions for Visual Tracking

In this paper, we analyze the spatial information of deep features, and propose two complementary regressions for robust visual tracking. First, we propose a kernelized ridge regression model wherein the kernel value is defined as the weighted sum of similarity scores of all pairs of patches between two samples. We show that this model can be formulated as a neural network and thus can be efficiently solved. Second, we propose a fully convolutional neural network with spatially regularized kernels, through which the filter kernel corresponding to each output channel is forced to focus on a specific region of the target. Distance transform pooling is further exploited to determine the effectiveness of each output channel of the convolution layer. The outputs from the kernelized ridge regression model and the fully convolutional neural network are combined to obtain the ultimate response. Experimental results on two benchmark datasets validate the effectiveness of the proposed method.Comment: To appear in CVPR201

Impact of template backbone heterogeneity on RNA polymerase II transcription.

Variations in the sugar component (ribose or deoxyribose) and the nature of the phosphodiester linkage (3'-5' or 2'-5' orientation) have been a challenge for genetic information transfer from the very beginning of evolution. RNA polymerase II (pol II) governs the transcription of DNA into precursor mRNA in all eukaryotic cells. How pol II recognizes DNA template backbone (phosphodiester linkage and sugar) and whether it tolerates the backbone heterogeneity remain elusive. Such knowledge is not only important for elucidating the chemical basis of transcriptional fidelity but also provides new insights into molecular evolution. In this study, we systematically and quantitatively investigated pol II transcriptional behaviors through different template backbone variants. We revealed that pol II can well tolerate and bypass sugar heterogeneity sites at the template but stalls at phosphodiester linkage heterogeneity sites. The distinct impacts of these two backbone components on pol II transcription reveal the molecular basis of template recognition during pol II transcription and provide the evolutionary insight from the RNA world to the contemporary 'imperfect' DNA world. In addition, our results also reveal the transcriptional consequences from ribose-containing genomic DNA

Nanowire Photoelectrochemistry.

Recent applications of photoelectrochemistry at the semiconductor/liquid interface provide a renewable route of mimicking natural photosynthesis and yielding chemicals from sunlight, water, and air. Nanowires, defined as one-dimensional nanostructures, exhibit multiple unique features for photoelectrochemical applications and promise better performance as compared to their bulk counterparts. This article reviews the use of semiconductor nanowires in photoelectrochemistry. After introducing fundamental concepts essential to understanding nanowires and photoelectrochemistry, the review considers answers to the following questions: (1) How can we interface semiconductor nanowires with other building blocks for enhanced photoelectrochemical responses? (2) How are nanowires utilized for photoelectrochemical half reactions? (3) What are the techniques that allow us to obtain fundamental insights of photoelectrochemistry at single-nanowire level? (4) What are the design strategies for an integrated nanosystem that mimics a closed cycle in artificial photosynthesis? This framework should help readers evaluate the salient features of nanowires for photoelectrochemical applications, promoting the sustainable development of solar-powered chemical plants that will benefit our society in the long run