A Gang of Adversarial Bandits

We consider running multiple instances of multi-armed bandit (MAB) problems in parallel. A main motivation for this study are online recommendation systems, in which each of N users is associated with a MAB problem and the goal is to exploit users' similarity in order to learn users' preferences to K items more efficiently. We consider the adversarial MAB setting, whereby an adversary is free to choose which user and which loss to present to the learner during the learning process. Users are in a social network and the learner is aided by a-priori knowledge of the strengths of the social links between all pairs of users. It is assumed that if the social link between two users is strong then they tend to share the same action. The regret is measured relative to an arbitrary function which maps users to actions. The smoothness of the function is captured by a resistance-based dispersion measure Ψ. We present two learning algorithms, GABA-I and GABA-II which exploit the network structure to bias towards functions of low Ψ values. We show that GABA-I has an expected regret bound of O(pln(N K/Ψ)ΨKT) and per-trial time complexity of O(K ln(N)), whilst GABA-II has a weaker O(pln(N/Ψ) ln(N K/Ψ)ΨKT) regret, but a better O(ln(K) ln(N)) per-trial time complexity. We highlight improvements of both algorithms over running independent standard MABs across users

Online Learning of Facility Locations

In this paper, we provide a rigorous theoretical investigation of an online learning version of the Facility Location problem which is motivated by emerging problems in real-world applications. In our formulation, we are given a set of sites and an online sequence of user requests. At each trial, the learner selects a subset of sites and then incurs a cost for each selected site and an additional cost which is the price of the user’s connection to the nearest site in the selected subset. The problem may be solved by an application of the well-known Hedge algorithm. This would, however, require time and space exponential in the number of the given sites, which motivates our design of a novel quasi-linear time algorithm for this problem, with good theoretical guarantees on its performance

MaxHedge: Maximising a Maximum Online

We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to maximise the cumulative profit, where the profit obtained on a single trial is defined as the difference between the maximum reward among the selected actions and the sum of their costs. Action energy values and the budget are known and fixed. All rewards and costs associated with each action change over time and are revealed at each trial only after the learner’s selection of actions. Our framework encompasses several online learning problems where the environment changes over time; and the solution trades-off between minimising the costs and maximising the maximum reward of the selected subset of actions, while being constrained to an action energy budget. The algorithm that we propose is efficient and general that may be specialised to multiple natural online combinatorial problems

New Insights into the Role of MHC Diversity in Devil Facial Tumour Disease

Devil facial tumour disease (DFTD) is a fatal contagious cancer that has decimated Tasmanian devil populations. The tumour has spread without invoking immune responses, possibly due to low levels of Major Histocompatibility Complex (MHC) diversity in Tasmanian devils. Animals from a region in north-western Tasmania have lower infection rates than those in the east of the state. This area is a genetic transition zone between sub-populations, with individuals from north-western Tasmania displaying greater diversity than eastern devils at MHC genes, primarily through MHC class I gene copy number variation. Here we test the hypothesis that animals that remain healthy and tumour free show predictable differences at MHC loci compared to animals that develop the disease

Ex situ diet influences the bacterial community associated with the skin of red-eyed tree frogs (Agalychnis callidryas)

Amphibians support symbiotic bacterial communities on their skin that protect against a range of infectious pathogens, including the amphibian chytrid fungus. The conditions under which amphibians are maintained in captivity (e.g. diet, substrate, enrichment) in ex situ conservation programmes may affect the composition of the bacterial community. In addition, ex situ amphibian populations may support different bacterial communities in comparison to in situ populations of the same species. This could have implications for the suitability of populations intended for reintroduction, as well as the success of probiotic bacterial inoculations intended to provide amphibians with a bacterial community that resists invasion by the chytrid fungus. We aimed to investigate the effect of a carotenoid-enriched diet on the culturable bacterial community associated with captive red-eyed tree frogs (Agalychnis callidryas) and make comparisons to bacteria isolated from a wild population from the Chiquibul Rainforest in Belize. We successfully showed carotenoid availability influences the overall community composition, species richness and abundance of the bacterial community associated with the skin of captive frogs, with A. callidryas fed a carotenoid-enriched diet supporting a greater species richness and abundance of bacteria than those fed a carotenoid-free diet. Our results suggest that availability of carotenoids in the diet of captive frogs is likely to be beneficial for the bacterial community associated with the skin. We also found wild A. callidryas hosted more than double the number of different bacterial species than captive frogs with very little commonality between species. This suggests frogs in captivity may support a reduced and diverged bacterial community in comparison to wild populations of the same species, which could have particular relevance for ex situ conservation projects

Influence of grinding on graphite crystallinity from experimental and natural data: implications for graphite thermometry and sample preparation

This paper examines the effects of shear stress on the structuralparameters that define the ‘crystallinity’ of graphite. The results show that highly crystalline graphite samples ground for up to 120 min do not undergo detectable changes in the three-dimensional arrangement of carbon layers but crystallite sizes (Lc and La) decrease consistently with increasing grinding time. Grinding also involves particle-size diminution that results in lower temperatures for the beginning of combustion and exothermic maxima in the differentialthermalanal ysis curves. These changes in the structuraland thermalcharacteristics of graphite upon grinding must be taken into account when such data are used for geothermometric estimations. Tectonic shear stress also induces reduction of the particle size and the Lc and La values of highly crystalline graphite. Thus, the temperature of formation of graphite according to structural as well as thermaldata is underestimated by up to 100ºC in samples that underwent the most intense shear stress. Therefore, application of graphite geothermometry to fluid-deposited veins where graphite is the only mineralfound should take into consideration the effect of tectonic shearing, or the estimated temperatures must be considered as minimum temperatures of formation only