Lessons from an unplanned scientific and academic life

It is salutary, before reaching the middle of one’s eighties, whilst time is still available and memory is still in good order, to review a long life in its highlights, so as to better appreciate the circumstances that shaped and steered that life through its many days. Besides this appreciation, such a review permits a listing of lessons learned through that life, its joys as well as its woes, in the hope that they may be useful to young readers of your story. Like all other such stories, mine was the story of an individual, who lived under unique circumstances and reacted to them in a unique way. My story is best treated in terms of where it was experienced, that being: Malta (1929 to 1952), Oxford (1952 to 1956), Singapore (1956 to 1960), Khartoum (1960 to 1965), and Saskatoon (1965 to the time of writing). Each transition was necessitated by its own circumstance, brought fresh challenges and sustained a global career with few regrets and much personal and professional satisfaction.peer-reviewe

Bethe Projections for Non-Local Inference

Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the mean field family of variational inference algorithms, soft- or hard-constrained inference using Lagrangian relaxation or linear programming, collective graphical models, and forms of semi-supervised learning such as posterior regularization. We present a method to discriminatively learn broad families of inference objectives, capturing powerful non-local statistics of the latent variables, while maintaining tractable and provably fast inference using non-Euclidean projected gradient descent with a distance-generating function given by the Bethe entropy. We demonstrate the performance and flexibility of our method by (1) extracting structured citations from research papers by learning soft global constraints, (2) achieving state-of-the-art results on a widely-used handwriting recognition task using a novel learned non-convex inference procedure, and (3) providing a fast and highly scalable algorithm for the challenging problem of inference in a collective graphical model applied to bird migration.Comment: minor bug fix to appendix. appeared in UAI 201

A Fault Analytic Method against HB+

The search for lightweight authentication protocols suitable for low-cost RFID tags constitutes an active and challenging research area. In this context, a family of protocols based on the LPN problem has been proposed: the so-called HB-family. Despite the rich literature regarding the cryptanalysis of these protocols, there are no published results about the impact of fault analysis over them. The purpose of this paper is to fill this gap by presenting a fault analytic method against a prominent member of the HB-family: HB+ protocol. We demonstrate that the fault analysis model can lead to a flexible and effective attack against HB-like protocols, posing a serious threat over them

Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory

We utilize machine learning models which are based on recurrent neural networks to optimize dynamical decoupling (DD) sequences. DD is a relatively simple technique for suppressing the errors in quantum memory for certain noise models. In numerical simulations, we show that with minimum use of prior knowledge and starting from random sequences, the models are able to improve over time and eventually output DD-sequences with performance better than that of the well known DD-families. Furthermore, our algorithm is easy to implement in experiments to find solutions tailored to the specific hardware, as it treats the figure of merit as a black box.Comment: 18 pages, comments are welcom