Managing Exchange Rate Volatility: A Comparative Counterfactual Analysis of Singapore 1994 to 2003

The objective of this paper is see how well Singapore’s exchange rate regime has coped with exchange rate volatility before and after the Asian financial crisis by comparing the performance of Singapore’s actual regime in minimising the volatility of the nominal effective exchange rate (NEER) and the bilateral rate against the US$ against some counterfactual regimes and the corresponding performance of eight other East Asian countries. In contrast to previous counterfactual exercises, such as Williamson (1998a) and Ohno (1999) which compute the weights for effective exchange rates on the basis of simple bloc aggregates, we apply a more disaggregated methodology using a larger number of trade partners. We also utilize ARCH/GARCH techniques to obtain estimates of heteroskedastic variances to better capture the time-varying characteristics of volatility for the actual and simulated exchange rate regimes. Our findings confirm that Singapore’s managed floating exchange rate system has delivered relatively low currency volatility. Although there are gains in volatility reduction for all countries in the sample from the adoption of either a unilateral or common basket peg, particularly post-crisis, these gains are relatively low for Singapore, largely because low actual volatility. Finally, there are additional gains for nondollar peggers from stabilizing intra-EA exchange rates against the dollar if they were to adopt a basket peg, especially post-crisis, but the gains for Singapore are again relatively modest.East Asia, exchange rates, counterfactuals.

Witten Diagrams for Torus Conformal Blocks

We give a holographic description of global conformal blocks in two dimensional conformal field theory on the sphere and on the torus. We show that the conformal blocks for one-point functions on the torus can be written as Witten diagrams in thermal AdS. This is accomplished by deriving a general conformal Casimir equation for global conformal blocks, and showing that Witten diagrams obey the same equation. We study the semi-classical limit of n-point conformal blocks, and show that these equal the action of a network of bulk world-lines obeying appropriate geodesic equations. We give an alternate description in the Chern-Simons formulation of 3D gravity, where the conformal blocks are described by networks of Wilson lines, and argue that these formulations are equivalent.Comment: 32 pages, 6 figure

Towards Multi-Modal Data Classification

A feature fusion multi-modal neural network (MMN) is a network that combines different modalities at the feature level to perform a specific task. In this paper, we study the problem of training the fusion procedure for MMN. A recent study has found that training a multi-modal network that incorporates late fusion produces a network that has not learned the proper parameters for feature extraction. These late fusion models perform very well during training but fall short to its single modality counterpart when testing. We hypothesize that jointly trained MMN have weight space that is too large for effective training. To remedy this problem, we design a set of procedures that systematically narrow the search space so that the optimizer would only consider weights that are known to generalize well. As part of our systematic narrowing procedure, we enforce a weight constraint on the weights between the pre-fusion and fusion layers. Due to our given constraints on the network, modern methods cannot optimize our network without breaking our conditions. To remedy the problem, we create a simplex projection module that will be used after applying modern training frameworks. Our module will re-optimize our network such that the weight constraints are enforced. This new framework, which we call Projection Feature Mixture Model outperforms its single modality model as well as standard jointly trained MMN. In this paper, we provide a theoretical analysis to show advantages of utilizing MMN

Web Tap Payment Authentication and Encryption With Zero Customer Effort

We propose a public-key authentication and encryption application that secures the messages between Tap-Card-Pay application, Tap-Card-Pay Systems Corporation, customers, and merchants allowing the customer to complete transactions without requiring the customer to input sensitive information. With authentication and encryption, the application transfers the credit card information from the smartphone\u27s near field communication device onto the merchant webpage. Security weaknesses are also presented to show how to attack this design

Pneumatic-probe measurement errors caused by fluctuating flow angles

Pneumatic probes such as five-hole probes (5HP) can conveniently measure three-dimensional flow angles, plus total and static pressure. In most applications, transducers are connected using pneumatic tubes, allowing the probe head to be highly miniaturized and robust. However, such “steady” probes are often used in unsteady flows, where they measure a pneumatically averaged flowfield that can differ from the time mean. To better understand these pneumatic averaging effects, an analytical framework is constructed using a quasi-steady model. Total and static pressure coefficients have a symmetric response to both positive and negative incidence. When incidence fluctuates, there is therefore a bias in the pneumatic average. These errors are evident in a shedding wake experiment, where a 5HP overestimates total pressure loss by up to 44% compared to a Kiel probe. These effects can be predicted by coupling an unsteady Reynolds-averaged Navier–Stokes calculation with the quasi-steady model. By predicting pneumatic averaging errors, the quasi-steady model can be used to obtain like-for-like validation of calculations against experimental data. Measurement data can also be corrected, provided that flow angle fluctuations can be measured or estimated. This approach can be readily used to postcorrect the large body of historical data likely to have been corrupted by pneumatic-averaging errors

Development of large radii half-wave plates for CMB satellite missions

The successful European Space Agency (ESA) Planck mission has mapped the Cosmic Microwave Background (CMB) temperature anisotropy with unprecedented accuracy. However, Planck was not designed to detect the polarised components of the CMB with comparable precision. The BICEP2 collaboration has recently reported the first detection of the B-mode polarisation. ESA is funding the development of critical enabling technologies associated with B-mode polarisation detection, one of these being large diameter half-wave plates. We compare different polarisation modulators and discuss their respective trade-offs in terms of manufacturing, RF performance and thermo-mechanical properties. We then select the most appropriate solution for future satellite missions, optimized for the detection of B-modes.Comment: 16 page

Colouring Australia: a participatory open data platform

Colouring Australia is a digital platform for collecting and visualising building level information across several Australian cities. It provides a valuable resource for bringing together data on building age, material, sustainability ratings, walkability and other key metrics as we plan for net zero cities. Colouring Australia comprises part of the international Colouring Cities Research Programme, which supports the development of open-source platforms that provide open data on national building stocks. In this paper we outline the technical architecture of the platform, and the development and visualisation of a building level walkability metric based on pedestrian access to destinations. This platform provides a useful digital tool for planners to understand which parts of the city are walkable and in turn this can support future active transport programs and policies. Future research will be to validate this novel walkability index through a series of stakeholder and public workshops using the Colouring Australia platform in an interactive tabletop environment where usability testing can be undertaken

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible