LeoTask: a fast, flexible and reliable framework for computational research

LeoTask is a Java library for computation-intensive and time-consuming research tasks. It automatically executes tasks in parallel on multiple CPU cores on a computing facility. It uses a configuration file to enable automatic exploration of parameter space and flexible aggregation of results, and therefore allows researchers to focus on programming the key logic of a computing task. It also supports reliable recovery from interruptions, dynamic and cloneable networks, and integration with the plotting software Gnuplot

Hybrid Epidemics - A Case Study on Computer Worm Conficker

Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading, local, neighbourhood and global to capture the worm's spreading behaviour. The parameters of the model are inferred directly from network data obtained during the first day of the Conifcker epidemic. The model is then used to explore the trade-off between spreading modes in determining the worm's effectiveness. Our results show that the Conficker epidemic is an example of a critically hybrid epidemic, in which the different modes of spreading in isolation do not lead to successful epidemics. Such hybrid spreading strategies may be used beneficially to provide the most effective strategies for promulgating information across a large population. When used maliciously, however, they can present a dangerous challenge to current internet security protocols

Quasi-Newton Methods for Markov Chain Monte Carlo

The performance of Markov chain Monte Carlo methods is often sensitive to the scaling and correlations between the random variables of interest. An important source of information about the local correlation and scale is given by the Hessian matrix of the target distribution, but this is often either computationally expensive or infeasible. In this paper we propose MCMC samplers that make use of quasi-Newton approximations, which approximate the Hessian of the target distribution from previous samples and gradients generated by the sampler. A key issue is that MCMC samplers that depend on the history of previous states are in general not valid. We address this problem by using limited memory quasi-Newton methods, which depend only on a fixed window of previous samples. On several real world datasets, we show that the quasi-Newton sampler is more effective than standard Hamiltonian Monte Carlo at a fraction of the cost of MCMC methods that require higher-order derivatives.

Boston Hospitality Review: Winter 2018

Table of contents: Blockchain Technology & its Implications for the Hospitality Industry By Tarik Dogru, Makarand Mody, & Christie Leonardi -- How Does My Neighbor Feel About my Airbnb? By Makarand Mody, Courtney Suess & Tarik Dogru -- 5 Keys to Successful Hospitality Leadership By Sarah Andersen -- Cutting Through the Online Hospitality Clutter: 10 Best Practices for Organic Visibility By Leora Lanz & Juan Lesmes -- When is a Group a Chain, and a Chain a Brand? By Christopher Muller -- À la Carte Dining in a Banquet Setting: Is it Feasible? By Peter Szende and Ally Run

Risks to human and animal health related to the presence of deoxynivalenol and its acetylated and modified forms in food and feed

Peer reviewedPublisher PD

Risks for public health related to the presence of tetrodotoxin (TTX) and TTX analogues in marine bivalves and gastropods

Peer reviewedPublisher PD