The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .
In this paper, we develop a novel unified framework called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN). First, we propose the inception region proposal network (Inception-RPN) and design a set of text characteristic prior bounding boxes to achieve high word recall with only hundred level candidate proposals. Next, we present a powerful text detection network that embeds ambiguous text category (ATC) information and multilevel region-of-interest pooling (MLRP) for text and non-text classification and accurate localization. Finally, we apply an iterative bounding box voting scheme to pursue high recall in a complementary manner and introduce a filtering algorithm to retain the most suitable bounding box, while removing redundant inner and outer boxes for each text instance. Our approach achieves an F-measure of 0.83 and 0.85 on the ICDAR 2011 and 2013 robust text detection benchmarks, outperforming previous state-of-the-art results.
Chiral β-amino alcohols are privileged scaffolds frequently found in pharmaceutically active molecules and natural products. Aminohydroxylation of olefins is one of the most powerful strategies to access chiral vicinal amino alcohols. However, the direct regio-and stereoselective aminohydroxylation of olefins to unprotected enantioenriched β-amino alcohols remains a longstanding challenge. Herein, we report that a novel one-pot four-enzyme [styrene monooxygenase (SMO)/epoxide hydrolase (EH)/ alcohol dehydrogenase (ADH)/ω-transaminase (TA)] biocatalytic cascade efficiently catalyzes the direct transformation of readily available styrenyl olefins into unprotected 2-amino-2-phenyl ethanols in good yields and excellent enantioselectivity. In vitro cascade biocatalysis aminohydroxylation of styrenyl olefins was first investigated by the combined four enzymes (SMO/EH/ADH/TA) with a trace amount of NADH (0.02 mM) and pyridoxal-5′-phosphate (0.1 mM), affording both enantiomers of β-amino alcohols 5a−j in 13.9−98.7% conversions and 86−99% ee. Whole-cell-based cascade biocatalysis was achieved by using the constructed recombinant Escherichia coli pairwise combinations and single tailor-made whole-cell biocatalyst without an additional NADH cofactor; (R)-and (S)-β-amino alcohols 5a−j could be obtained in 14.6−99.7% conversions and 86−99% ee. Moreover, the preparative experiments of this new cascade biocatalysis were demonstrated by the single tailor-made whole-cell biocatalyst [E. coli (CGS-DEM) and E. coli (CGS-DEB)] with the substrates 1a−b and 1h in an aqueous-organic two-phase system, affording chiral βamino alcohols [(R)-or (S)-5a−b, h] in good yields (50.9−64.3%) and excellent ee (>99%).
ABSTRACT. The reductive amination of cyclohexanone with ammonia over Cu-Cr-La/γ-Al2O3 was investigated. It was found that a proper solvent with high solubility of ammonia and 4Å molecular sieves for the elimination of generated water contributed to the formation of cyclohexylamine in the premixing process. In addition, the addition of ammonia in the fixedbed reactor could obviously improve the conversion of cyclohexanone to cyclohexylamine. Finally, reaction conditions including reaction temperature, hydrogen pressure and charging rate of the premix were optimized. Under the optimized conditions, cyclohexylamine was obtained in 83.06% yield.
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