Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured by the edge weights. Given a target number of nodes to label, the goal is to choose those nodes that are most informative and then predict the unknown labels. We propose a novel framework for this problem based on our recent results on sampling theory for graph signals. A graph signal is a real-valued function defined on each node of the graph. A notion of frequency for such signals can be defined using the spectrum of the graph Laplacian matrix. The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices. This approach allows us to define a criterion for active learning based on sampling set selection which aims at maximizing the frequency of the signals that can be reconstructed from their samples on the set. Experiments show the effectiveness of our method.Comment: 10 pages, 6 figures, To appear in KDD'1

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

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

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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