A High-Fidelity Realization of the Euclid Code Comparison NN-body Simulation with Abacus

We present a high-fidelity realization of the cosmological $N$-body simulation from the Schneider et al. (2016) code comparison project. The simulation was performed with our Abacus $N$-body code, which offers high force accuracy, high performance, and minimal particle integration errors. The simulation consists of $2048^3$ particles in a $500\ h^{-1}\mathrm{Mpc}$ box, for a particle mass of $1.2\times 10^9\ h^{-1}\mathrm{M}_\odot$ with $10\ h^{-1}\mathrm{kpc}$spline softening. Abacus executed 1052 global time steps to$z=0$in 107 hours on one dual-Xeon, dual-GPU node, for a mean rate of 23 million particles per second per step. We find Abacus is in good agreement with Ramses and Pkdgrav3 and less so with Gadget3. We validate our choice of time step by halving the step size and find sub-percent differences in the power spectrum and 2PCF at nearly all measured scales, with$<0.3\%$errors at$k<10\ \mathrm{Mpc}^{-1}h$. On large scales, Abacus reproduces linear theory better than$0.01\%$. Simulation snapshots are available at http://nbody.rc.fas.harvard.edu/public/S2016 .Comment: 13 pages, 8 figures. Minor changes to match MNRAS accepted versio

Exchange rate volatility, employment and macroeconomic dynamics in South Africa

Includes bibliographical referencesThis thesis focuses on the effects and causes of exchange rate volatility in South Africa. These issues are analysed in three stand-alone but related papers. The first paper (Chapter 2) investigates the impact of real exchange rate volatility on employment growth in the manufacturing sector. The study contributes to the literature on the employment effects of exchange rate volatility in emerging markets given limited studies. This is done by using the Autoregressive Distributed Lag (ARDL) counteraction approach which is able to estimate an error correction form of the model for the variables under investigation. This enables one to analyse the relationship between exchange rate volatility and employment growth. The advantage of this approach is that it performs better in small samples and works well even when the underlying variables are integrated of different orders. Employing quarterly time series data for the period 1995 . 2010, the analysis shows that real exchange rate volatility has a significant contractionary effect on manufacturing employment growth. The study also provides evidence that exchange rate level, output, wages and interest rates have significant effects on manufacturing employment growth. The results suggest that the government can reduce the adverse effects of exchange rate volatility on manufacturing by adopting macroeconomic policies that minimise exchange rate volatility and policies that promote employment creation, for instance, less restrictive policies given that the results show that an increase in interest rates leads to a decline in employment. Coming up with macroeconomic policies that minimise exchange rate volatility requires the knowledge of the causes of exchange rate volatility. As a result, the second paper (Chapter 3) investigates the determinants of exchange rate volatility in South Africa. Few studies investigate the determinants of rand volatility (Arezki, Dumitrescu, Freytag & Quintyn 2014, Farrell 2001). This study contributes to the literature by finding the sources of rand volatility using output volatility, money supply volatility, foreign reverses volatility, commodity price volatility, openness and a dummy for capital account liberalisation as explanatory variables. This is done using GARCH models for the period 1986- 2013 employing monthly time series data. The advantage of GARCH models is that they are able to model and forecast time-varying variance given that the exchange rate behaves similarly to other asset prices, for example, stock prices. The study tests the hypothesis that economic openness leads to a reduction in exchange rate volatility following Hau's (2002) modifications of the New Open Macroeconomics model of Obstfeld & Rogoff (1995, 1996). South Africa is a good case study following the liberalisation of the capital account in March 1995. The results show that switching to a coating exchange rate regime has a significant positive effect on exchange rate volatility. That is, it increases exchange rate volatility. The results also show that trade openness reduces exchange rate volatility using the bilateral exchange rate. The results also show that output, commodity prices, money supply and foreign reserves volatilities significantly influences exchange rate volatility. The study also shows that real factors (commodity prices, output and openness) have relatively larger effects on exchange rate volatility compared to monetary factors. The third paper (Chapter 4) analyses the short run behaviour of the South African rand using daily data. The study contributes to the literature on the causes of exchange rate movements in several ways. First, it uses an event studies approach a la Campbell, Lo & MacKinlay (1997) to answer two research questions. First, what is the impact of South Africa's monetary policy announcements on the rand? Second, what is the impact of South African political events on the rand? The advantage of event studies is that they are able to quantify systematically the abnormal or unexpected impact of an economic or political event on asset prices like the exchange rate. Second, the study focuses on an emerging market given that most studies have mainly focused on developed economies. Third, few studies that use event studies in South Africa focus on stock market reaction to announcements. The results find 8 out of 12 significant cumulative abnormal returns for monetary policy announcements. This suggests that the rand is not only influenced by demand and supply flows but also by news. The study also finds significant cumulative abnormal returns for all the three exchange rates following the Marikana massacre on 16 August 2012 and the release of Nelson Mandela banknotes on 6 November 2012. The ANC elective conference only has significant cumulative abnormal returns using the Rand/US dollar in 2007 and 2012

Viewpoints: A high-performance high-dimensional exploratory data analysis tool

Scientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets, but as the price of commodity graphics hardware has dropped and its capability has increased, it is now possible, in principle, to view large complex data sets on a single workstation. To do this in practice, an investigator will need software that is written to take advantage of the relevant graphics hardware. The Viewpoints visualization package described herein is an example of such software. Viewpoints is an interactive tool for exploratory visual analysis of large, high-dimensional (multivariate) data. It leverages the capabilities of modern graphics boards (GPUs) to run on a single workstation or laptop. Viewpoints is minimalist: it attempts to do a small set of useful things very well (or at least very quickly) in comparison with similar packages today. Its basic feature set includes linked scatter plots with brushing, dynamic histograms, normalization and outlier detection/removal. Viewpoints was originally designed for astrophysicists, but it has since been used in a variety of fields that range from astronomy, quantum chemistry, fluid dynamics, machine learning, bioinformatics, and finance to information technology server log mining. In this article, we describe the Viewpoints package and show examples of its usage.Comment: 18 pages, 3 figures, PASP in press, this version corresponds more closely to that to be publishe

Geo-tagging and privacy-preservation in mobile cloud computing

With the emerge of the cloud computing service and the explosive growth of the mobile devices and applications, mobile computing technologies and cloud computing technologies have been drawing significant attentions. Mobile cloud computing, with the synergy between the cloud and mobile technologies, has brought us new opportunities to develop novel and practical systems such as mobile multimedia systems and cloud systems that provide collaborative data-mining services for data from disparate owners (e.g., mobile users). However, it also creates new challenges, e.g., the algorithms deployed in the computationally weak mobile device require higher efficiency, and introduces new problems such as the privacy concern when the private data is shared in the cloud for collaborative data-mining. The main objectives of this dissertation are: 1. to develop practical systems based on the unique features of mobile devices (i.e., all-in-one computing platform and sensors) and the powerful computing capability of the cloud; 2. to propose solutions protecting the data privacy when the data from disparate owners are shared in the cloud for collaborative data-mining. We first propose a mobile geo-tagging system. It is a novel, accurate and efficient image and video based remote target localization and tracking system using the Android smartphone. To cope with the smartphones' computational limitation, we design light-weight image/video processing algorithms to achieve a good balance between estimation accuracy and computational complexity. Our system is first of its kind and we provide first hand real-world experimental results, which demonstrate that our system is feasible and practicable. To address the privacy concern when data from disparate owners are shared in the cloud for collaborative data-mining, we then propose a generic compressive sensing (CS) based secure multiparty computation (MPC) framework for privacy-preserving collaborative data-mining in which data mining is performed in the CS domain. We perform the CS transformation and reconstruction processes with MPC protocols. We modify the original orthogonal matching pursuit algorithm and develop new MPC protocols so that the CS reconstruction process can be implemented using MPC. Our analysis and experimental results show that our generic framework is capable of enabling privacy preserving collaborative data-mining. The proposed framework can be applied to many privacy preserving collaborative data-mining and signal processing applications in the cloud. We identify an application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We further propose a privacy preserving storage and secure watermark detection framework by adopting our generic framework to address such a requirement. In our secure watermark detection framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. We also give mathematical and statistical analysis to derive the expected watermark detection performance in the compressive sensing domain, based on the target image, watermark pattern and the size of the compressive sensing matrix (but without the actual CS matrix), which means that the watermark detection performance in the CS domain can be estimated during the watermark embedding process. The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. By taking advantage of our mobile geo-tagging system and compressive sensing based privacy preserving data-mining framework, we develop a mobile privacy preserving collaborative filtering system. In our system, mobile users can share their personal data with each other in the cloud and get daily activity recommendations based on the data-mining results generated by the cloud, without leaking the privacy and secrecy of the data to other parties. Experimental results demonstrate that the proposed system is effective in enabling efficient mobile privacy preserving collaborative filtering services.Includes bibliographical references (pages 126-133)