A survey on generative adversarial networks for imbalance problems in computer vision tasks

Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms

Utilization of phosphorus for casein biosynthesis in the mammary gland. II. Incorporation of P<SUP>32</SUP> into free phosphopeptides of milk and of mammary gland

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Molten Salt Thermal Energy Storage Systems

The feasibility of storing thermal energy at temperatures of 450 C to 535 C in the form of latent heat of fusion was examined for over 30 inorganic salts and salt mixtures. Alkali carbonate mixtures were chosen as phase-change storage materials in this temperature range because of their relatively high storage capacity and thermal conductivity, moderate cost, low volumetric expansion upon melting, low corrosivity, and good chemical stability. Means of improving heat conduction through the solid salt were explored

Racial Disparities in Necrotizing Enterocolitis.

Necrotizing enterocolitis (NEC) is a serious disease of the intestinal tract affecting 5-10% of pre-term infants with up to 50% mortality in those that require surgery. There is wide variation in the rates and outcomes of NEC by race and ethnicity, and the reasons for this disparity are poorly understood. In this article, we review the epidemiology and discuss possible explanations for racial and ethnic differences in NEC. Most of the current evidence investigating the role of race in NEC comes from North America and suggests that Hispanic ethnicity and non-Hispanic Black race are associated with higher risk of NEC compared to non-Hispanic White populations. Differences in pre-term births, breastfeeding rates, and various sociodemographic factors does not fully account for the observed disparities in NEC incidence and outcomes. While genetic studies are beginning to identify candidate genes that may increase or decrease risk for NEC among racial populations, current data remain limited by small sample sizes and lack of validation. Complex interactions between social and biological determinants likely underly the differences in NEC outcomes among racial groups. Larger datasets with detailed social, phenotypic, and genotypic information, coupled with advanced bioinformatics techniques are needed to comprehensively understand racial disparities in NEC

Intraclass image augmentation for defect detection using generative adversarial neural networks

Surface defect identification based on computer vision algorithms often leads to inadequate generalization ability due to large intraclass variation. Diversity in lighting conditions, noise components, defect size, shape, and position make the problem challenging. To solve the problem, this paper develops a pixel-level image augmentation method that is based on image-to-image translation with generative adversarial neural networks (GANs) conditioned on fine-grained labels. The GAN model proposed in this work, referred to as Magna-Defect-GAN, is capable of taking control of the image generation process and producing image samples that are highly realistic in terms of variations. Firstly, the surface defect dataset based on the magnetic particle inspection (MPI) method is acquired in a controlled environment. Then, the Magna-Defect-GAN model is trained, and new synthetic image samples with large intraclass variations are generated. These synthetic image samples artificially inflate the training dataset size in terms of intraclass diversity. Finally, the enlarged dataset is used to train a defect identification model. Experimental results demonstrate that the Magna-Defect-GAN model can generate realistic and high-resolution surface defect images up to the resolution of 512 × 512 in a controlled manner. We also show that this augmentation method can boost accuracy and be easily adapted to any other surface defect identification models

Sensitivity and kinetics of signal transmission at the first visual synapse differentially impact visually-guided behavior

In the retina, synaptic transmission between photoreceptors and downstream ON-bipolar neurons (ON-BCs) is mediated by a GPCR pathway, which plays an essential role in vision. However, the mechanisms that control signal transmission at this synapse and its relevance to behavior remain poorly understood. In this study we used a genetic system to titrate the rate of GPCR signaling in ON-BC dendrites by varying the concentration of key RGS proteins and measuring the impact on transmission of signal between photoreceptors and ON-BC neurons using electroretinography and single cell recordings. We found that sensitivity, onset timing, and the maximal amplitude of light-evoked responses in rod- and cone-driven ON-BCs are determined by different RGS concentrations. We further show that changes in RGS concentration differentially impact visually guided-behavior mediated by rod and cone ON pathways. These findings illustrate that neuronal circuit properties can be modulated by adjusting parameters of GPCR-based neurotransmission at individual synapses

A test architecture design for SoCs using ATAM method

Test arranging is a basic issue in structure on-a-chip (S.O.C) experiment mechanization. Capable investigation designs constrain the general organization check request time, keep away from analysis reserve conflicts, in addition to purpose of restriction control disseminating in the midst of examination manner. In this broadsheet, we absent a fused method to manage a couple of test arranging issues. We first present a system to choose perfect timetables for sensibly evaluated SOC’s among need associations, i.e., plans that spare alluring orderings among tests. This furthermore acquaints a capable heuristic estimation with plan examinations designed for enormous S.O.Cs through need necessities in polynomial occasion. We portray a narrative figuring with the purpose of uses pre-emption of tests to secure capable date-books in favour of SOCs. Exploratory marks on behalf of an educational S-O-C plus a cutting edge SOC exhibit with the aim of capable investigation timetables be able to subsist gained in sensible CPU occasion

Characterization of nanometer scale compositionally inhomogeneous AlGaN active regions on bulk AlN substrates

The optical and structural properties of AlGaN active regions containing nanoscale compositional inhomogeneities (NCI) grown on low dislocation density bulk AlN substrates are reported. These substrates are found to improve the internal quantum efficiency and structural quality of NCI-AlGaN active regions for high Al content alloys, as well as the interfaces of the NCI with the surrounding wider bandgap matrix, as manifested in the absence of any significant long decay component of the low temperature radiative lifetime, which is well characterized by a single exponential photoluminescence decay with a 330 ps time constant. However, room temperature results indicate that non-radiative recombination associated with the high point defect density becomes a limiting factor in these films even at low dislocation densities for larger AlN mole fractions

Attention guided multi-task learning for surface defect identification

Surface defect identification is an essential task in the industrial quality control process, in which visual checks are conducted on a manufactured product to ensure that it meets quality standards. Convolutional Neural Network (CNN) based surface defect identification method has proven to outperform traditional image processing techniques. However, the real-world surface defect datasets are limited in size due to the expensive data generation process and the rare occurrence of defects. To address this issue, this paper presents a method for exploiting auxiliary information beyond the primary labels to improve the generalization ability of surface defect identification tasks. Considering the correlation between pixel level segmentation masks, object level bounding boxes and global image level classification labels, we argue that jointly learning features of the related tasks can improve the performance of surface defect identification tasks. This paper proposes a framework named Defect-Aux-Net, based on multi-task learning with attention mechanisms that exploit the rich additional information from related tasks with the goal of simultaneously improving robustness and accuracy of the CNN based surface defect identification. We conducted a series of experiments with the proposed framework. The experimental results showed that the proposed method can significantly improve the performance of state-of-the-art models while achieving an overall accuracy of 97.1%, Dice score of 0.926 and mAP of 0.762 on defect classification, segmentation and detection tasks

Design and performance evaluation of a lightweight wireless early warning intrusion detection prototype

The proliferation of wireless networks has been remarkable during the last decade. The license-free nature of the ISM band along with the rapid proliferation of the Wi-Fi-enabled devices, especially the smart phones, has substantially increased the demand for broadband wireless access. However, due to their open nature, wireless networks are susceptible to a number of attacks. In this work, we present anomaly-based intrusion detection algorithms for the detection of three types of attacks: (i) attacks performed on the same channel legitimate clients use for communication, (ii) attacks on neighbouring channels, and (iii) severe attacks that completely block network's operation. Our detection algorithms are based on the cumulative sum change-point technique and they execute on a real lightweight prototype based on a limited resource mini-ITX node. The performance evaluation shows that even with limited hardware resources, the prototype can detect attacks with high detection rates and a few false alarms. © 2012 Fragkiadakis et al