•Use non-realistic computer graphics to generate training samples for object detection.•Investigate the impact of context when training deep models with synthetic samples.•Experiments are performed ...in several well-known traffic light datasets.•Our approach achieves results comparable to those that use real-world training data.
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Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights. However, acquiring and annotating real data can be extremely costly in terms of time and effort. In this context, we propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds that are not related to the traffic domain. Thus, a large amount of training data can be generated without annotation efforts. Furthermore, it also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state. Experiments show that it is possible to achieve results comparable to those obtained with real training data from the problem domain, yielding an average mAP and an average F1-score which are each nearly 4 p.p. higher than the respective metrics obtained with a real-world reference model.
Digital reconstruction of mechanically shredded documents has received increasing attention in the last years mainly for historical and forensics needs. Computational methods to solve this problem ...are highly desirable in order to mitigate the time-consuming human effort and to preserve document integrity. The reconstruction of strips-shredded documents is accomplished by horizontally splicing pieces so that the arising sequence (solution) is as similar as the original document. In this context, a central issue is the quantification of the fitting between the pieces (strips), which generally involves stating a function that associates a pair of strips to a real value indicating the fitting quality. This problem is also more challenging for text documents, such as business letters or legal documents, since they depict poor color information. The system proposed here addresses this issue by exploring character shapes as visual features for compatibility computation. Experiments conducted with real mechanically shredded documents showed that our approach outperformed in accuracy other popular techniques in the literature considering documents with (almost) only textual content.
•An interactive framework for reconstruction of strip-shredded documents.•The user lock and forbid pairs automatically selected by the recommender module.•Four query strategies for recommending the ...pairs of shreds to be annotated.•A novel methodology to assess the human impact on the quality of a reconstruction.•Annotating 25% of the shreds can yield an error reduction of more than 40%.
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The advances in machine learning – particularly in deep learning – have enabled automatizing the reconstruction of shredded documents with significant accuracy. However, despite the recent remarkable results, the state-of-the-art on fully automatic reconstruction still has room for improvement, mainly due to imprecision on the evaluation of how the shreds fit each other (compatibility/cost evaluation). To tackle this problem, we propose a human-in-the-loop reconstruction framework that takes user inputs to improve the solutions (permutation of shreds). In our approach, the user verifies whether adjacent shreds of a solution are also adjacent in the original document. Unlike the current literature, our framework includes a recommender module that automatically selects pairs of shreds to be analyzed by a human. Four recommendation strategies were proposed and evaluated. Results achieved by coupling deep learning reconstruction methods into our framework have shown that introducing the human in the loop can reduce errors by more than 40%.
In the present article, we describe the synthesis, anti-HIV1 profile and molecular modeling evaluation of 11 oxoquinoline derivatives.
In the present article, we describe the synthesis, anti-HIV1 ...profile and molecular modeling evaluation of 11 oxoquinoline derivatives. The structure–activity relationship analysis revealed some stereoelectronic properties such as LUMO energy, dipole moment, number of rotatable bonds, and of hydrogen bond donors and acceptors correlated with the potency of compounds. We also describe the importance of substituents R
2 and R
3 for their biological activity. Compound
2j was identified as a lead compound for future investigation due to its : (i) high activity against HIV-1, (ii) low cytotoxicity in PBMC, (iii) low toxic risks based on in silico evaluation, (iv) a good theoretical oral bioavailability according to Lipinski ‘rule of five’, (v) higher druglikeness and drug-score values than current antivirals AZT and efavirenz.
The use of zeolite catalysts for biomass valorization has increasingly contributed to the development of more efficient and sustainable processes in recent times. ZSM-5 zeolites, in special, have ...displayed great potential as catalysts in such reactions owing to their promising features e.g. the tridimensional medium size pore system and the wide Si/Al ratio. Considering these facts, in this review we provide an outline of lignocellulosic biomass upgrading processes using ZSM-5 zeolites in their acidic form and exchanged with transition metals; we will cover not only the direct conversion of lignocellulosic biomass, but also the upgrading of its derivatives.
•Zeolites have been receiving rising attention in recent times in both the petrochemical industry and biomass valorization.•ZSM-5 zeolites display promising features for applications in biomass upgrading.•In this review we provide an outline of lignocellulosic biomass upgrading using ZSM-5 zeolites
The cover picture illustrates the fabrication process of electrochemical paper‐based analytical devices (ePADs) fabricated by pencil drawing for detecting analgesics and sedation drugs in whiskey. ...Analgesics are commonly used to adulterate alcoholic beverages to prevent the hangover. On other hand, sedative drugs are occasionally added to alcoholic beverages to promote loss of consciousness, amnesia and hallucination in the victim. The ePADs fabricated with graphite pencil were successfully tested to detect metamizole, paracetamol and midazolam maleate, as model drugs, in whiskey samples. Due to the inherent portability, disposability, low cost, operational simplicity and speed, ePADs can emerge as an attractive strategy for point‐of‐need by crime scene or police agents. More details are discussed in the article by Anderson A. Dias, Thiago M. G. Cardoso, Cyro L. S. Chagas, Virgílio X. G. Oliveira, Rodrigo A. A. Munoz, Charles S. Henry, Mário H. P. Santana, Thiago R. L. C. Paixão and Wendell K. T. Coltro. DOI: 10.1002/elan.201800308
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