Optical Information Transmission and Multimode Fluorescence Anticounterfeiting of Ca2-xMgxGe7O16:Mn2.

Multimode emission of Mn2+ for multimode fluorescence anticounterfeiting is achieved by cation site and interstitial occupancy in Ca2-xMgxGe7O16. The rings in Ca2-xMgxGe7O16 have a significant distortion for Mn2+ ions to enter the ring interstitials with a luminescence center at 665 nm, which is supported by XRD refinement results and first-principles calculations. The interstitial Mn2+ ion has good thermal stability with an activation energy of 0.36 eV. Surprisingly, these two luminescence centers, the cation site Mn and the interstitial Mn, have an obvious afterglow, and the disappearing afterglow will reappear by heating or irradiating with the 980 nm laser. The afterglow is significantly enhanced, as MnO2 is used as the manganese source, which is explained in detail by the thermal luminescence spectrum. Finally, Ca2-xMgxGe7O16:Mn2+ fully demonstrates its excellent prospects in fluorescent anticounterfeiting, information encryption, and optical information storage.

Domain Adaptation Preconceived Hashing for Unconstrained Visual Retrieval.

Learning to hash has been widely applied for image retrieval due to the low storage and high retrieval efficiency. Existing hashing methods assume that the distributions of the retrieval pool (i.e., the data sets being retrieved) and the query data are similar, which, however, cannot truly reflect the real-world condition due to the unconstrained visual cues, such as illumination, pose, background, and so on. Due to the large distribution gap between the retrieval pool and the query set, the performances of traditional hashing methods are seriously degraded. Therefore, we first propose a new efficient but transferable hashing model for unconstrained cross-domain visual retrieval, in which the retrieval pool and the query sample are drawn from different but semantic relevant domains. Specifically, we propose a simple yet effective unsupervised hashing method, domain adaptation preconceived hashing (DAPH), toward learning domain-invariant hashing representation. Three merits of DAPH are observed: 1) to the best of our knowledge, we first propose unconstrained visual retrieval by introducing DA into hashing for learning transferable hashing codes; 2) a domain-invariant feature transformation with marginal discrepancy distance minimization and feature reconstruction constraint is learned, such that the hashing code is not only domain adaptive but content preserved; and 3) a DA preconceived quantization loss is proposed, which further guarantees the discrimination of the learned hashing code for sample retrieval. Extensive experiments on various benchmark data sets verify that our DAPH outperforms many state-of-the-art hashing methods toward unconstrained (unrestricted) instance retrieval in both single- and cross-domain scenarios.

Deep-Like Hashing-in-Hash for Visual Retrieval: An Embarrassingly Simple Method.

Existing hashing methods have yielded significant performance in image and multimedia retrieval, which can be categorized into two groups: shallow hashing and deep hashing. However, there still exist some intrinsic limitations among them. The former generally adopts a one-step strategy to learn the hashing codes for discovering the discriminative binary feature, but the latent discriminative information in the learned hashing codes is not well exploited. The latter, as deep neural network based hashing models, can learn highly discriminative and compact features, but relies on large-scale data and computation resources for numerous network parameters tuning with back-propagation optimization. Straightforward training of deep hashing models from scratch on small-scale data is almost impossible. Therefore, in order to develop efficient but effective learning to hash algorithm that depends only on small-scale data, we propose a novel non-neural network based deep-like learning framework, i.e. multi-level cascaded hashing (MCH) approach with hierarchical learning strategy, for image retrieval. The contributions are threefold. First, a hashing-in-hash architecture is designed in MCH, which inherits the excellent traits of traditional neural networks based deep learning, such that discriminative binary features that are beneficial to image retrieval can be effectively captured. Second, in each level the binary features of all preceding levels and the visual appearance feature are simultaneously cascaded as inputs of all subsequent levels to retrain, which fully exploits the implicated discriminative information. Third, a basic learning to hash (BLH) model with label constraint is proposed for hierarchical learning. Without loss of generality, the existing hashing models can be easily integrated into our MCH framework. We show experimentally on small- and large-scale visual retrieval tasks that our method outperforms several state-of-the-arts.

Factors dominating 3-dimensional ozone distribution during high tropospheric ozone period.

Data from an in situ monitoring network and five ozone sondes are analysed during August of 2012, and a high tropospheric ozone episode is observed around the 8th of AUG. The Community Multi-scale Air Quality (CMAQ) model and its process analysis tool were used to study factors and mechanisms for high ozone mixing ratio at different levels of ozone vertical profiles. A sensitive scenario without chemical initial and boundary conditions (ICBCs) from MOZART4-GEOS5 was applied to study the impact of stratosphere-troposphere exchange (STE) on vertical ozone. The simulation results indicated that the first high ozone peak near the tropopause was dominated by STE. Results from process analysis showed that: in the urban area, the second peak at approximately 2 km above ground height was mainly caused by local photochemical production. The third peak (near surface) was mainly caused by the upwind transportation from the suburban/rural areas; in the suburban/rural areas, local photochemical production of ozone dominated the high ozone mixing ratio from the surface to approximately 3 km height. Furthermore, the capability of indicators to distinguish O3-precursor sensitivity along the vertical O3 profiles was investigated. Two sensitive scenarios, which had cut 30% anthropogenic NOX or VOC emissions, showed that O3-precursor indicators, specifically the ratios of O3/NOy, H2O2/HNO3 or H2O2/NOZ, could partly distinguish the O3-precursor sensitivity between VOCs-sensitive and NOx-sensitive along the vertical profiles. In urban area, the O3-precursor relationship transferred from VOCs-sensitive within the boundary layer to NOx-sensitive at approximately 1-3 km above ground height, further confirming the dominant roles of transportation and photochemical production in high O3 peaks at the near-ground layer and 2 km above ground height, respectively.

Effect of Post Treatment For Cu-Cr Source/Drain Electrodes on a-IGZO TFTs.

We report a high-performance amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) thin-film transistor (TFT) with new copper-chromium (Cu-Cr) alloy source/drain electrodes. The TFT shows a high mobility of 39.4 cm 2 ·V - 1 ·s - 1 a turn-on voltage of -0.8 V and a low subthreshold swing of 0.47 V/decade. Cu diffusion is suppressed because pre-annealing can protect a-IGZO from damage during the electrode sputtering and reduce the copper diffusion paths by making film denser. Due to the interaction of Cr with a-IGZO, the carrier concentration of a-IGZO, which is responsible for high mobility, rises.