Generative modelling and adversarial learning

University of Technology Sydney. Faculty of Engineering and Information Technology.A main goal of statistics and machine learning is to represent and manipulate high-dimensional probability distributions of real-world data, such as natural images. Generative adversarial networks (GAN), which are based on the adversarial learning paradigm, are one of the main types of methods for deriving generative models from complicated real-world data. GAN and its variants use a generator to synthesise semantic data from standard signal distributions and train a discriminator to distinguish real samples in the training dataset from fake samples synthesised by the generator. As a confronter, the generator aims to deceive the discriminator by producing ever more realistic samples. Through a two-player adversarial game played by the generator and discriminator, the generated distribution can approximate the real-world distribution and generate samples from it. This thesis aims to both improve the quality of generative modelling and manipulate generated samples by specifying multiple scene properties. A novel framework for training GAN is proposed to stabilise the training process and produce more realistic samples. Unlike existing GANs, which alternately train a generator and a discriminator using a pre-defined adversarial objective function, different adversarial training objectives are utilised as mutation operations and train a population of generators to adapt to the environment (i.e. the discriminator). The samples generated by different iterations of generators are evaluated and only well-performing generators are preserved and used for further training. In this way, the proposed framework overcomes the limitations of an individual adversarial training objective and always preserves the best offspring, contributing to the progress and success of GANs. Based on the GANs framework, this thesis devised a novel model, called a perceptual adversarial network (PAN). The proposed PAN consists of two feed-forward convolutional neural networks: a transformation network and a discriminative network. Besides generative adversarial loss, which is widely used in GANs, this thesis proposes to employ perceptual adversarial loss, which undergoes adversarial training between the transformation network and hidden layers of the discriminative network. The hidden layers and output of the discriminative network are upgraded to constantly and automatically discover discrepancies between a transformed image and the corresponding ground truth, and the image transformation network is trained to minimise the discrepancy identified by the discriminative network. Furthermore, to extend the generative models to perform more challenging re-rendering tasks, this thesis explores disentangled representations encoded in real-world samples and proposes a principled tag disentangled generative adversarial network for re-rendering new samples of the object of interest from a single image by specifying multiple scene properties. Specifically, from an input sample, a disentangling network extracts disentangled and interpretable representations, which are then used to generate new samples using the generative network. In order to improve the quality of the disentangled representations, a tag mapping net determines the consistency between the image and its tags. Finally, experiments with different challenging datasets and image synthesis tasks demonstrate the good performance of the proposed frameworks regarding the problem of interest

True or False Prosperity? The Effect of Token Incentives in Decentralized Autonomous Organizations

Decentralized autonomous organizations (DAO) received many discussions and attempts recently with the rapid development of blockchain. Token incentive is one of its most important features and owns multiple attributes of equity, property, and currency. To explore its unknown effect, we utilize a quasi-experiment setting in the NFT marketplaces. We find that the token incentives with DAO implementation in Rarible can significantly motivate users’ participation compared with SuperRare at the platform level. At the seller level, by the comparison of cross-platform users and only-OpenSea users, we find it significantly changes users’ trading behavior which reflects in the increment in transactions number and average prices. However, through the equilibrium analysis based on the supply and demand model, the growth rate of the average prices is far beyond the magnitude it should be at the equilibrium state. Therefore, we argue that buyers’ purchase decision is driven by the high expectations of token value

Three-Dimensional Modelling and Simulation of the Ice Accretion Process on Aircraft Wings

© 2018 Chang S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In this article, a new computational method for the three-dimensional (3D) ice accretion analysis on an aircraft wing is formulated and validated. The two-phase flow field is calculated based on Eulerian-Eulerian approach using standard dispersed turbulence model and second order upwind differencing with the aid of commercial software Fluent, and the corresponding local droplet collection efficiency, convective heat transfer coefficient, freezing fraction and surface temperature are obtained. The classical Messinger model is modified to be capable of describing 3D thermodynamic characteristics of ice accretion. Considering effects of runback water, which is along chordwise and spanwise direction, an extended Messinger method is employed for the prediction of the 3D ice accretion rates. Validation of the newly developed model is carried out through comparisons with available experimental ice shape and LEWICE codes over a GLC-305 wing under both rime and glaze icing conditions. Results show that good agreement is achieved between the current computational ice shapes and the compared results. Further calculations based on the proposed method over a M6 wing under different test conditions are numerically demonstrated.Peer reviewedFinal Published versio

Grid-Aware On-Route Fast-Charging Infrastructure Planning for Battery Electric Bus with Equity Considerations: A Case Study in South King County

The transition from traditional bus fleets to zero-emission ones necessitates the development of effective planning models for battery electric bus (BEB) charging infrastructure. On-route fast charging stations, distinct from on-base charging stations, present unique challenges related to safe operation and power supply capacity, making it difficult to control grid operational costs. This paper establishes a novel framework that integrates the bus route network and power network, which leverages the inter-dependency between both networks to optimize the planning outcomes of on-route BEB charging stations in South King County. The problem is formulated as a mixed-integer second-order cone programming model, aiming to minimize the overall planning cost, which includes investments in charging equipment, power facility, and grid operation. Furthermore, fairness measurements are incorporated into the planning process, allowing for the consideration of both horizontal transit equity and vertical transit equity based on different zone merging criteria within the county's existing census tracts. The results of this planning model offer valuable insights into achieving both economic efficiency and social justice in the design of on-route charging facilities for BEBs in South King County.Comment: 18 pages, 16 figure