Efficient RkNN Retrieval with Arbitrary Non-Metric Similarity Measures

A RkNN query returns all objects whose nearest k neighbors
contain the query object. In this paper, we consider RkNN
query processing in the case where the distances between
attribute values are not necessarily metric. Dissimilarities
between objects could then be a monotonic aggregate of dissimilarities
between their values, such aggregation functions
being specified at query time. We outline real world cases
that motivate RkNN processing in such scenarios. We consider
the AL-Tree index and its applicability in RkNN query
processing. We develop an approach that exploits the group
level reasoning enabled by the AL-Tree in RkNN processing.
We evaluate our approach against a Naive approach
that performs sequential scans on contiguous data and an
improved block-based approach that we provide. We use
real-world datasets and synthetic data with varying characteristics
for our experiments. This extensive empirical
evaluation shows that our approach is better than existing
methods in terms of computational and disk access costs,
leading to significantly better response times.

Integrated techno-economic and environmental assessments of sixty scenarios for co-firing biomass with coal and natural gas

Displacement of fossil fuel-based power through biomass co-firing could reduce the greenhouse gas (GHG) emissions from fossil fuels. In this study, data-intensive techno-economic models were developed to evaluate different co-firing technologies as well as the configurations of these technologies. The models were developed to study 60 different scenarios involving various biomass feedstocks (wood chips, wheat straw, and forest residues) co-fired either with coal in a 500 MW subcritical pulverized coal (PC) plant or with natural gas in a 500 MW natural gas combined cycle (NGCC) plant to determine their technical potential and costs, as well as to determine environmental benefits. The results obtained reveal that the fully paid-off coal-fired power plant co-fired with forest residues is the most attractive option, having levelized costs of electricity (LCOE) of $53.12–$54.50/MW h and CO2 abatement costs of $27.41–$31.15/tCO2. When whole forest chips are co-fired with coal in a fully paid-off plant, the LCOE and CO2 abatement costs range from $54.68 to $56.41/MW h and $35.60 to $41.78/tCO2, respectively. The LCOE and CO2 abatement costs for straw range from $54.62 to $57.35/MW h and $35.07 to $38.48/tCO2, respectively.

Case report:Evidence of rise in rabies cases in southern Malawi--better preventative measures are urgently required

We describe five children who died of clinical rabies in a three month period (September to November 2011) in the Queen Elizabeth Central Hospital. From previous experience and hospital records, this number of cases is higher than expected. We are concerned that difficulty in accessing post-exposure prophylaxis (PEP) rabies vaccine may be partly responsible for this rise. We advocate: (a) prompt course of active immunisation for all patients with significant exposure to proven or suspected rabid animals. (b) the use of an intradermal immunisation regime that requires a smaller quantity of the vaccine than the intramuscular regime and gives a better antibody response. (c) improved dog rabies control measures.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.