Data augmentation using generative adversarial networks for electrical insulator anomaly detection

Master of ScienceDepartment of Computer ScienceWilliam H. HsuElectricity has been an essential part of our life. Insulators, which are widely used for electricity transmission, are prone to be damaged and need constant maintenance. Traditionally, the inspection job is time-consuming and dangerous as workers would have to climb up the electricity tower. Deep learning has offered a safe and quick way to inspections. About 3000 insulators images are taken from different angles using a drone. Due to great difference in number of good and damaged insulator, directly training a classifier on the imbalanced data lead to low recall value on the damaged insulators. Generative adversarial networks (GANs) were introduced as a novel way to augment data. However, traditional GANs are either incapable of generating high quality images or fail to generate minority class images when minority class examples are far less. In this study, a novel GAN model, Balancing and Progressive GANs (BPGANs), was proposed for effectively making use of all classes information and generating high quality minority images at the same time. Results show that PGANs, StyleGANs, and BPGANs were able to generate high-resolution images and improve classification performance. PGANs achieved the better results than BPGANs. This may be because BPGANs only provides 2 additional latent codes since it is a binary classification, having little effect on generating desired images. BPGANs seemed to have difficulties generating class-specific images, which might be because that the classification loss is too little compared to the source loss and optimization was more focused to optimize the source loss. This indicates that learning representations of data progressively from low resolution to high resolution is an effective approach, however, embedding class label information in the fashion of AC-GANs and BGANs might not be appropriate for augmenting binary class data sets

Biologics for the treatment of chronic rhinosinusitis with nasal polyps : state of the art

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a complex upper airway disease affecting up to 11% of the population of Western Europe. In these western countries, 85% of the CRSwNP disease reveals a type 2 inflammatory pattern. In the last 15 years, several randomized double-blind studies on monoclonal antibodies in CRSwNP were performed. These studies demonstrated for the first time that biologics targeting type 2 immune reactions might be successful in nasal polyps. The target proteins, interleukin (IL)-4, IL-5, IL-13, and IgE were previously identified as key mediators in studies using nasal polyp tissues to measure and to interact in ex-vivo settings. No biomarkers have been identified to predict response to a specific biologic or to monitor treatment success. These studies were characterized by small numbers of patients and heterogeneous populations. They did, however, pave the way for currently performed and analyzed phase 3 studies, which will possibly lead to the registration of the first biologic drug with the indication CRSwNP. The studies already provide indications on the effects to be expected from those biologics; the results of phase-3 studies in larger populations will be decisive for the indications, patient selection, and finally the stopping rules for those drugs in subjects with severe nasal polyps, in whom the current standard of care including topical and oral glucocorticosteroids, antibiotics and surgical procedures failed to control the disease. We may expect that those biologics will open new perspectives for those patients with severe polyposis with, but also independent of asthma, allowing to avoid the possible adverse events resulting from systemic glucocorticosteroids and surgery

A Computational Model of the Short-Cut Rule for 2D Shape Decomposition

We propose a new 2D shape decomposition method based on the short-cut rule. The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts. We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition. The model we proposed generates a set of cut hypotheses passing through the points on the silhouette which represent the negative minima of curvature. We then show that most part-cut hypotheses can be eliminated by analysis of local properties of each. Finally, the remaining hypotheses are evaluated in ascending length order, which guarantees that of any pair of conflicting cuts only the shortest will be accepted. We demonstrate that, compared with state-of-the-art shape decomposition methods, the proposed approach achieves decomposition results which better correspond to human intuition as revealed in psychological experiments.Comment: 11 page

Determinants Of Stock Option Use By Chinese Companies

Using a sample of 225 stock option grants over the period January 2006 to June 2013, we examine the economic determinants of stock option use in Chinese firms from the optimal contract and managerial power approaches. We investigate whether the same economic factors can explain stock option awards to different types of target grantees (including directors and senior executives, technical and business personnel, and special talents introduced in the future). In consistent with the optimal contract theory, we find that the scope of stock option plans is negatively associated with ?rm size, dividend dummy, and three ownership measures (managerial ownership, blockholder ownership, and foreign ownership). Furthermore, we find that the scope of stock option plans is positively related to book-to-market ratio and prior stock returns, but the coefficients are significant only when the stock options awards cover senior managers. We also find that the impact of risk is different when options are targeted to different types of employees. In consistent with the managerial power theory, we ?nd that the scope of stock option plans is inversely related to state ownership. As for the other economic factors, their degree of impact is found to be different across a broad base of employees. In general, ownership variables are more relevant to key technical and business personnel, while firm characteristics variables are more relevant to top management