Tuning Jeff = 1/2 Insulating State via Electron Doping and Pressure in Double-Layered Iridate Sr3Ir2O7

Sr3Ir2O7 exhibits a novel Jeff=1/2 insulating state that features a splitting between Jeff=1/2 and 3/2 bands due to spin-orbit interaction. We report a metal-insulator transition in Sr3Ir2O7 via either dilute electron doping (La3+ for Sr2+) or application of high pressure up to 35 GPa. Our study of single-crystal Sr3Ir2O7 and (Sr1-xLax)3Ir2O7 reveals that application of high hydrostatic pressure P leads to a drastic reduction in the electrical resistivity by as much as six orders of magnitude at a critical pressure, PC = 13.2 GPa, manifesting a closing of the gap; but further increasing P up to 35 GPa produces no fully metallic state at low temperatures, possibly as a consequence of localization due to a narrow distribution of bonding angles {\theta}. In contrast, slight doping of La3+ ions for Sr2+ ions in Sr3Ir2O7 readily induces a robust metallic state in the resistivity at low temperatures; the magnetic ordering temperature is significantly suppressed but remains finite for (Sr0.95La0.05)3Ir2O7 where the metallic state occurs. The results are discussed along with comparisons drawn with Sr2IrO4, a prototype of the Jeff = 1/2 insulator.Comment: five figure

Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos

What makes content go viral? Which videos become popular and why others don't? Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, accessible for regular users, that leverage these theoretical results. HIPie -- an interactive visualization system -- is created to fill this gap, by enabling users to reason about the virality and the popularity of online videos. It retrieves the metadata and the past popularity series of Youtube videos, it employs Hawkes Intensity Process, a state-of-the-art online popularity model for explaining and predicting video popularity, and it presents videos comparatively in a series of interactive plots. This system will help both content consumers and content producers in a range of data-driven inquiries, such as to comparatively analyze videos and channels, to explain and predict future popularity, to identify viral videos, and to estimate response to online promotion

Part-Based Deep Hashing for Large-Scale Person Re-Identification

© 1992-2012 IEEE. Large-scale is a trend in person re-identi-fication (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture. Each triplet sample contains two pedestrian images (or parts) with the same identity and one pedestrian image (or part) of the different identity. A triplet loss function is employed with a constraint that the Hamming distance of pedestrian images (or parts) with the same identity is smaller than ones with the different identity. In the experiment, we show that the proposed PDH method yields very competitive re-id accuracy on the large-scale Market-1501 and Market-1501+500K datasets

Changes in the metabolome of Picea balfouriana embryogenic tissues that were linked to different levels of 6-BAP by gas chromatography-mass spectrometry approach

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