Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}

Utilization of Landsat-8 data for the estimation of carrot and maize crop water footprint under the arid climate of Saudi Arabia

Understanding the spatial variability of Water Footprint (WF) of crops is essential for the efficient use of the available water resources. Therefore, this study was designed to bridge the gap in knowledge existed in the area of WF in the arid climate of Saudi Arabia by quantifying the remote sensing based blue-WF (WFblue) of maize and carrot crops cultivated during the period from December 2015 to December 2016. Agrometeorological (empirical) estimated WF components, namely, the WFblue, the green-WF (WFgreen) and the grey-WF (WFgrey), were determined at a farm scale in conjunction with the climatic conditions and cropping patterns. On the other hand, the WFBlue was estimated from Landsat-8 data using energy balance and yield models. The empirical approach based WFBlue was used as a reference for the accuracy assessment of the Landsat-8 estimated WFBlue. The empirically estimated WF of silage maize ranged from 3540 m3 t-1 to 4960 m3 t-1. Out of which the WFgreen, the WFblue and the WFgrey composed 0.74%, 83.28% and 15.98%, respectively. For the carrot crop; however, the WF ranged between 2970 m3 t-1 and 5020 m3 t-1. Where, the WFgreen, the WFblue and the WFgrey represented 0.50%, 77.31% and 22.19%, respectively. Using Landsat-8 data, the WFblue was found to vary across the crops from 2552 m3 t-1 (silage maize) to 3010 m3 t-1 (carrot). Results also revealed a highly significant linear relationship between the empirical and the Landsat-8 derived WFBlue (R2 = 0.77, P>F = 0.001). The utility of Landsat-8 data in mapping WF showed reliable seasonal estimates, which can greatly enhance precision management practices of irrigation water

Anti-tissue transglutaminase antibodies in inflammatory and degenerative arthropathies

Recent studies identified tissue transglutaminase (tTG) as the antigen eliciting antiendomysial antibodies (EMA) in celiac disease (CD). Anti-tTG antibodies have therefore been proposed as a serological test for CD. Nevertheless, IgA anti-tTG but not EMA have also been found in inflammatory bowel disease patients, suggesting that these antibodies are linked to a tissue lesion rather than to an auto-immune component of CD. To confirm this hypothesis, we evaluated the presence of IgA anti-tTG in patients with inflammatory and degenerative diseases, in whom tissue lesions presented far away from the intestinal mucosa. The study was carried out on the serum and synovial fluid (SF) of 68 patients with rheumatoid arthritis (RA=33), psoriatic arthritis (PsA=26) and osteoarthritis (OA=9). In RA, PsA and OA sera, IgA anti-tTG were positive in 33%, 42% and 11% of patients, respectively. Serum anti-tTG levels were significantly higher in RA (p<0.0001), PsA (p<0.0001) and OA (p<0.02) with respect to healthy controls. SF anti-tTG levels were significantly higher in PsA (p<0.018) than in OA. A good correlation between serum and synovial fluid anti-tTG levels was found in all arthropathies This study suggests that tTG is not the only antigen of EMA and, furthermore , that IgA anti-tTG antibodies represent a general lesion-associated event. Moreover, the significant correlation between serum and synovial fluid anti-tTG levels allow us to hypothesise that these antibodies could be synthesized in the site of arthritic lesions

Improvements in Raman Lidar Measurements Using New Interference Filter Technology

Narrow-band interference filters with improved transmission in the ultra-violet have been developed under NASA-funded research and used in the Raman Airborne Spectroscopic Lidar (RASL) in ground-based, upward-looking tests. Measurements were made of atmospheric water vapor, cirrus cloud optical properties and carbon dioxide that improve upon any previously demonstrated using Raman lidar. Daytime boundary and mixed layer profiling of water vapor mixing ratio up to an altitude of approximately 4 h is performed with less than 5% random error using temporal and spatial resolution of 2-minutes and 60 - 210, respectively. Daytime cirrus cloud optical depth and extinction-to-backscatter ratio measurements are made using 1 -minute average. Sufficient signal strength is demonstrated to permit the simultaneous profiling of carbon dioxide and water vapor mixing ratio into the free troposphere during the nighttime. A description of the filter technology developments is provided followed by examples of the improved Raman lidar measurements

Defining Configurable Virtual Reality Templates for End Users

This paper proposes a solution for supporting end users in configuring Virtual Reality environments by exploiting reusable templates created by experts. We identify the roles participating in the environment development and the means for delegating part of the behaviour definition to the end users. We focus in particular on enabling end users to define the environment behaviour. The solution exploits a taxonomy defining common virtual objects having high-level actions for specifying event-condition-Action rules readable as natural language sentences. End users exploit such actions to define the environment behaviour. We report on a proof-of-concept implementation of the proposed approach, on its validation through two different case studies (virtual shop and museum), and on evaluating the approach with expert users