A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset, called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201

Therapeutic collaboration and resistance: describing the nature and quality of the therapeutic relationship within ambivalence events using the therapeutic collaboration coding system

We understand ambivalence as a cyclical movement between two opposing parts of the self. The emergence of a novel part produces an innovative moment, challenging the current maladaptive self-narrative. However, the novel part is subsequently attenuated by a return to the maladaptive self-narrative. This study focused on the analysis of the therapeutic collaboration in episodes in which a relatively poor-outcome client in narrative therapy expressed ambivalence. Method: For our analysis we used the Therapeutic Collaboration Coding System, developed to assess whether and how the therapeutic dyad is working within the therapeutic zone of proximal development (TZPD). Results: Results showed that when the therapist challenged the client after the emergence of ambivalence, the client tended to invalidate (reject or ignore) the therapist’s intervention. Conclusions: This suggests that in such ambivalence episodes the therapist did not match the client’s developmental level, and by working outside the TZPD unintentionally contributed to the maintaining the client’s ambivalence

An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector

This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The system is conceived by evaluating and optimizing different models, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.9% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Genetic diversity of Xanthomonas citri subsp. citri in citrus orchards in northwest Paraná state, Brazil

Xanthomonas citri subsp. citri, which causes Asiatic citrus canker (ACC), is an important pathogen of citrus in Brazil and elsewhere. The genetic diversity of X. citri subsp. citri pathotype ‘A’ has not been studied in Brazil at a local scale (up to 300 km). Forty isolates were sampled from lesions of ACC on citrus in three orchards in Paraná state, Brazil. Twelve minisatellite markers were used to characterize the genetic diversity of the isolates. An Unweighted Paired Group Method of Arithmetic Averages tree was used for identifying unique multilocus haplotypes but there was no association between haplotypes and source locations. An analysis of molecular variance among populations showed that 98% of the variance was accounted for within the populations, and only 2% was accounted for among populations. Differences among populations was not significant (Φ=0.018, P=0.2). The relatively high, yet uniform, genetic diversity among isolates and low degree of spatial differentiation between populations of X. citri subsp. citri suggests that the populations in Paraná state have a common origin and strong historical epidemiological links

Unmet Diagnostic and Therapeutic Opportunities for COPD in Low- and Middle-Income Countries

RATIONALE: Chronic obstructive pulmonary disease (COPD) is a prevalent and burdensome condition in low- and middle-income countries (LMICs). Challenges to better care include more effective diagnosis, and access to affordable interventions. There are no previous reports describing therapeutic needs in LMIC populations with COPD identified through screening. OBJECTIVE: To describe unmet therapeutic need in screening-detected COPD in LMIC settings. METHODS: We compared interventions recommended by the international 'GOLD' COPD strategy document, with that received, in 1000 people with COPD identified by population screening at three LMIC sites in Nepal, Peru and Uganda. We calculated costs using data on the availability and affordability of medicines. MEASUREMENT AND MAIN RESULTS: The greatest unmet need for non-pharmacological interventions was for education and vaccinations (applicable to all), pulmonary rehabilitation (49%), smoking cessation (30%) and advice on biomass smoke exposure (26%). 95% of cases were previously undiagnosed and few were receiving therapy (4.5% had short-acting beta-agonists). Only three of 47 people (6%) with a previous COPD diagnosis had access to drugs consistent with recommendations. None of those with more severe COPD were accessing appropriate maintenance inhalers. Even when available, maintenance treatments were unaffordable with 30 days of treatment more than a low-skilled workers' daily average wage. CONCLUSION: We found significant missed opportunity to reduce the burden of COPD in LMIC settings, with most cases undiagnosed. Whilst there is unmet need in developing novel therapies, in LMICs where the burden is greatest, better diagnosis together with access to affordable interventions could translate to immediate benefit. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)

Bright and dark diffractive focusing

We investigate bright and dark diffractive focusing emerging in the free propagation of specific wave profiles. These general wave phenomena manifest themselves in matter, water, and classical waves. In this article, we lay the foundations for these effects and illustrate their origin in Wigner phase space. Our theoretical studies are supported by experimental demonstrations of dark focusing in water waves. Moreover, by using different phase slits we analyze several aspects of bright and dark focusing for classical and matter waves

The NuSTAR ULX program

We present the results of the first large program of broadband ULX observations with NuSTAR, XMM-Newton and Suzaku, yielding high-quality spectra and timing measurements from 0.3-30 keV in 6 ULXs, providing powerful information for understanding the accretion modes and nature of the central BHs. In particular, we find that all ULXs in our sample have a clear cutoff above 10 keV. This cutoff is less pronounced than expected by Comptonization from a cold, thick corona. We confirm the presence of a soft excess at low energies in the brightest ULXs, with temperatures below ~ 0.5 keV. We make an estimates on the masses of several ULXs based on spectral variability and model fitting