A system is described which digests large volumes of text, filtering out irrelevant articles and distilling the remainder into templates that represent information from the articles in simple slot/filler pairs. The system is highly modular in that it consists of a series of programs, each of which contributes information to the text to help in the final analysis of determining which strings con...

As the Internet continues to grow both in size and in terms of the volume of traffic it carries, more and more networks in the different parts of the world are relying on an increasing number of distinct ways to exchange traffic with one another. As a result, simple questions such as “What is the application mix in today’s Internet?” may produce non-informative simple answers unless they are re...

Distillation protocols enable generation of high quality entanglement even in the presence of noise. Existing protocols ignore the presence of local information in mixed states produced from some noise sources such as photon loss, amplitude damping or thermalization. We propose new protocols that exploit local information in mixed states. Our protocols converge to higher fidelities in fewer rou...

Most Higher Education institutions are now embracing e-learning to some extent. Some have instigated institution-wide e-learning initiatives, whilst others are engaging in the use of e-learning to expand their portfolio of course provision and the development of new niche markets. But the successful deployment of e-learning, whether large-scale or more localised, is complex and multi-faceted. T...

Speech recognition systems that combine multiple types of acoustic models have been shown to outperform single-model systems. However, such systems can be complex to implement and too resource-intensive to use in production. This paper describes how to use knowledge distillation to combine acoustic models in a way that has the best of many worlds: It improves recognition accuracy significantly,...

Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which...

Cross-lingual open information extraction is the task of distilling facts from the source language into representations in the target language. We propose a novel encoder-decoder model for this problem. It employs a novel selective decoding mechanism, which explicitly models the sequence labeling process as well as the sequence generation process on the decoder side. Compared to a standard enco...

Randomness extraction against side information is the art of distilling from a given source a key which is almost uniform conditioned on the side information. This paper provides randomness extraction against quantum side information whose extractable key length is given by a quantum generalization of the conditional collision entropy defined without the conventional smoothing. Based on the fac...

In this work, we study the expressive power of variants of Klaim, an experimental language with programming primitives for global computing that combines the process algebra approach with the coordination-oriented one. Klaim has proved to be suitable for programming a wide range of distributed applications with agents and code mobility, and has been implemented on the top of a runtime system ba...