Do Foreign Mergers & Acquisitions Boost Firm Productivity?

This paper examines the causal relationship between foreign mergers and acquisitions and firm productivity in the UK over the period 1999-2007. Our results raise questions about the existence of aggregate effects of foreign ownership on TFP in the longer-run. However, we find significant heterogeneity in the TFP effects of foreign M&A at the industry level. Overall, we uncover a systematic pattern of post-acquisition TFP effects that is consistent with the most recent theoretical models of firm heterogeneity and cross-border mergers and acquisitions as mode of foreign entry. Furthermore, we find positive aggregate effects on labor productivity due to capital deepening but not due to changes in TFP.Cross-border mergers and acquisitions; Productivity; Firm heterogeneity

A Parallel Mesh-Adaptive Framework for Hyperbolic Conservation Laws

We report on the development of a computational framework for the parallel, mesh-adaptive solution of systems of hyperbolic conservation laws like the time-dependent Euler equations in compressible gas dynamics or Magneto-Hydrodynamics (MHD) and similar models in plasma physics. Local mesh refinement is realized by the recursive bisection of grid blocks along each spatial dimension, implemented numerical schemes include standard finite-differences as well as shock-capturing central schemes, both in connection with Runge-Kutta type integrators. Parallel execution is achieved through a configurable hybrid of POSIX-multi-threading and MPI-distribution with dynamic load balancing. One- two- and three-dimensional test computations for the Euler equations have been carried out and show good parallel scaling behavior. The Racoon framework is currently used to study the formation of singularities in plasmas and fluids.Comment: late submissio

A Shared Task on Bandit Learning for Machine Translation

We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of a sequence of rounds, a machine translation system is required to propose a translation for an input, and receives a real-valued estimate of the quality of the proposed translation for learning. This paper describes the shared task's learning and evaluation setup, using services hosted on Amazon Web Services (AWS), the data and evaluation metrics, and the results of various machine translation architectures and learning protocols.Comment: Conference on Machine Translation (WMT) 201

Explaining the Great Moderation: Credit in the Macroeconomy Revisited

This study in recent history connects macroeconomic performance to financial policies in order to explain the decline in volatility of economic growth in the US since the mid-1980s, which is also known as the ‘Great Moderation’. Existing explanations attribute this to a combination of good policies, good environment, and good luck. This paper hypothesizes that before and during the Great Moderation, changes in the structure and regulation of US financial markets caused a redirection of credit flows, increasing the share of mortgage credit in total credit flows and facilitating the smoothing of volatility in GDP via equity withdrawal and a wealth effect on consumption. Institutional and econometric analysis is employed to assess these hypotheses. This yields substantial corroboration, lending support to a novel ‘policy’ explanation of the Moderation.real estate, macro volatility

Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

UK gas markets : the market price of risk and applications to multiple interruptible supply contracts.

We employ the Schwartz and Smith [Schwartz, E., and J. Smith, 2000, Short-term variations and long-term dynamics in commodity prices, Management Science 46, 893–911.] model to explore the dynamics of the UK gasmarkets. We discuss in detail the short-termand long-termmarket prices of risk borne by the market players and how deviations from expected cyclical storage affect the short-term market price of risk. Finally, we illustrate an application of the model by pricing interruptible supply contracts that are currently traded in the UKInterruptible supply contracts; Gas markets; Commodities; Market price of short-term and long-term risk; Multi-exercise Bermudan options; Convenience yield;

Semi-Supervised Learning with Scarce Annotations

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.Comment: Workshop on Deep Vision, CVPR 202

Competing for School Improvement Dollars: State Grant-Making Strategies

Outlines early findings about the the revamped School Improvement Grant program's impact on states and three approaches to evaluating district and school grant applications, including the use of external reviewers and cutoff scores. Makes recommendations