Low complexity method for large-scale self-consistent ab initio electronic-structure calculations without localization

A novel low complexity method to perform self-consistent electronic-structure calculations using the Kohn-Sham formalism of density functional theory is presented. Localization constraints are neither imposed nor required thereby allowing direct comparison with conventional cubically scaling algorithms. The method has, to date, the lowest complexity of any algorithm for an exact calculation. A simple one-dimensional model system is used to thoroughly test the numerical stability of the algorithm and results for a real physical system are also given

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

BOSH:Bayesian Optimization by Sampling Hierarchically

Deployments of Bayesian Optimization (BO) for functions with stochastic evaluations, such as parameter tuning via cross validation and simulation optimization, typically optimize an average of a fixed set of noisy realizations of the objective function. However, disregarding the true objective function in this manner finds a high-precision optimum of the wrong function. To solve this problem, we propose Bayesian Optimization by Sampling Hierarchically (BOSH), a novel BO routine pairing a hierarchical Gaussian process with an information-theoretic framework to generate a growing pool of realizations as the optimization progresses. We demonstrate that BOSH provides more efficient and higher-precision optimization than standard BO across synthetic benchmarks, simulation optimization, reinforcement learning and hyper-parameter tuning tasks

Anthracycline-trastuzumab regimens for HER2/neu-overexpressing breast cancer: current experience and future strategies

Anthracycline-trastuzumab-containing regimens demonstrate significant clinical activity in human epidermal growth factor receptor 2 (HER2)-positive breast cancer; however, the utility of this strategy is limited by unacceptably high rates of significant cardiotoxicity, particularly with concurrent administration. Anthracycline-induced cardiotoxicity is thought to be mediated primarily through increased myocardial oxidative stress, modified partly by the activity of neuregulins. Trastuzumab-induced cardiotoxicity is thought to be mediated by the ErbB/neuregulin system, with exposure to trastuzumab partly blocking the protective effect of neuregulins on the myocardium. As a result, trastuzumab increases the risk of anthracycline-induced cardiotoxicity. Several strategies have been adopted in attempts to minimize cardiotoxicity, including patient selection on the basis of preexisting cardiac risk, monitoring of cardiac function during treatment, and early management of cardiac dysfunction. The use of less cardiotoxic anthracyclines may be one strategy to lessen the risk of cardiotoxicity. Liposomal doxorubicin products offer similar efficacy compared with conventional doxorubicin, with significantly less cardiotoxicity, and have been successfully used in combination with trastuzumab in the metastatic and neo-adjuvant setting. Clinical trials are currently underway to assess the safety of pegylated liposomal doxorubicin during concurrent administration with trastuzumab compared with standard sequential treatment using conventional doxorubicin in the adjuvant settin

Early social adversity modulates the relation between attention biases and socioemotional behaviour in juvenile macaques

Affect-biased attention may play a fundamental role in early socioemotional development, but factors influencing its emergence and associations with typical versus pathological outcomes remain unclear. Here, we adopted a nonhuman primate model of early social adversity (ESA) to: (1) establish whether juvenile, pre-adolescent macaques demonstrate attention biases to both threatening and reward-related dynamic facial gestures; (2) examine the effects of early social experience on such biases; and (3) investigate how this relation may be linked to socioemotional behaviour. Two groups of juvenile macaques (ESA exposed and non-ESA exposed) were presented with pairs of dynamic facial gestures comprising two conditions: neutral-threat and neutral-lipsmacking. Attention biases to threat and lipsmacking were calculated as the proportion of gaze to the affective versus neutral gesture. Measures of anxiety and social engagement were also acquired from videos of the subjects in their everyday social environment. Results revealed that while both groups demonstrated an attention bias towards threatening facial gestures, a greater bias linked to anxiety was demonstrated by the ESA group only. Only the non-ESA group demonstrated a significant attention bias towards lipsmacking, and the degree of this positive bias was related to duration and frequency of social engagement in this group. These findings offer important insights into the effects of early social experience on affect-biased attention and related socioemotional behaviour in nonhuman primates, and demonstrate the utility of this model for future investigations into the neural and learning mechanisms underlying this relationship across development

BOSS: Bayesian Optimization over String Spaces

This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops. Recent applications of BO over strings have been hindered by the need to map inputs into a smooth and unconstrained latent space. Learning this projection is computationally and data-intensive. Our approach instead builds a powerful Gaussian process surrogate model based on string kernels, naturally supporting variable length inputs, and performs efficient acquisition function maximization for spaces with syntactical constraints. Experiments demonstrate considerably improved optimization over existing approaches across a broad range of constraints, including the popular setting where syntax is governed by a context-free grammar

Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition

This work draws attention to the large fraction of near-duplicates in the training and test sets of datasets widely adopted in License Plate Recognition (LPR) research. These duplicates refer to images that, although different, show the same license plate. Our experiments, conducted on the two most popular datasets in the field, show a substantial decrease in recognition rate when six well-known models are trained and tested under fair splits, that is, in the absence of duplicates in the training and test sets. Moreover, in one of the datasets, the ranking of models changed considerably when they were trained and tested under duplicate-free splits. These findings suggest that such duplicates have significantly biased the evaluation and development of deep learning-based models for LPR. The list of near-duplicates we have found and proposals for fair splits are publicly available for further research at https://raysonlaroca.github.io/supp/lpr-train-on-test/Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 202