Generate To Adapt: Aligning Domains using Generative Adversarial Networks

Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.Comment: Accepted as spotlight talk at CVPR 2018. Code available here: https://github.com/yogeshbalaji/Generate_To_Adap

Boosts, Schwarzschild Black Holes and Absorption cross-sections in M theory

$D$ dimensional neutral black strings wrapped on a circle are related to $(D-1)$ dimensional charged black holes by boosts. We show that the boost has to be performed in the covering space and the boosted coordinate has to be compactified on a circle with a Lorentz contracted radius. Using this fact we show that the transition between Schwarzschild black holes to black p-branes observed recently in M theory is the well-known black hole- black string transition viewed in a boosted frame. In a similar way the correspondence point where an excited string state goes over to a neutral black hole is mapped exactly to the correspondence point for black p-branes. In terms of the $p$ brane quantities the equation of state for an excited string state becomes identical to that of a 3+1 dimensional massless gas for all $p$. Finally, we show how boosts can be used to relate Hawking radiation rates. Using the known microscopic derivation of absorption by extremal 3-branes and near-extremal 5D holes with three large charges we provide a microscopic derivation of absorption of 0-branes by seven and five dimensional Schwarzschild black holes in a certain regime.Comment: Some references added, minor clarifications (harvmac, 16 pages

System-wide assessment of intervention strategies for railway infrastructure

The existing railway infrastructure exhibits faster degradation rates and requires more maintenance effort as a result of increased utilisation. At the same time, due to increased numbers of freight and commuter trains obtaining access to the railway infrastructure to carry out maintenance and repairs, not to mention major enhancements, is becoming more problematic. Furthermore, safety measures put in place in the event of infrastructure faults or delayed completion of intervention activities will cause greater disruptions to train services in the parts of the network with intense train traffic. Taking a system wide view is, therefore, vital for developing efficient intervention strategies that could deliver the desired infrastructure outputs. In this paper we propose a modelling approach for simulation and analysis of railway track asset management strategies integrating different elements of the whole railway system. The approach uses a Petri net modelling technique to construct the railway system model. The model is built in a hierarchical, modular fashion, meaning that the system can be represented at any level of granularity and complexity, ranging from a single-asset system in a small segment of the network to a complex multi-asset system in a large geographical region. The impact of different asset management strategies on the infrastructure functionality and the operation of train services is assessed using the Monte Carlo simulation technique

A holistic approach to railway infrastructure asset management

In the railway industry asset management decisions are focused on the maintenance, enhancement and renewal of assets in order to ensure a required level of dependability and improvement in services at the lowest whole life costs. To achieve these objectives system lifecycle models, rather than individual asset models,= offer a greater advantage. The paper presents a modelling approach developed for constructing multi asset system models to support well-informed railway infrastructure asset management decisions. The models are built using the Petri Net formalism and are analysed by a means of Monte Carlo simulations. A specific example of the railway superstructure model is presented. Its simulation results demonstrate the superiority of the system-wide model against individual asset models in terms of its accuracy in predicting the superstructure (system) performance and information available to support asset management decisions. Furthermore, by using the multi-asset system model interdependencies among maintenance regimes of different assets and different parts of the infrastructure can be modelled