Fast & Confident Probabilistic Categorization

We describe NRC's submission to the Anomaly Detection/Text Mining competition organised at the Text Mining Workshop 2007. This submission relies on a straightforward implementation of the probabilistic categoriser described in (Gaussier et al., ECIR'02). This categoriser is adapted to handle multiple labelling and a piecewise-linear confidence estimation layer is added to provide an estimate of the labelling confidence. This technique achieves a score of 1.689 on the test data

ICROFS news 2/2009 - newsletter from ICROFS

News from ICROFS: CORE Organic ERA-net proposal is formulated, FAO side-event was a success, upcoming course in media handling Articles: J. Eriksen, M. Askegaard & K. Søegaard: Nitrogen management on large organic daity farms C. Daugbjerg & K. M. Sønderskov: Organic labelling systems and consumer confidence G. T. Svendsen: Organic farmers can gain from Green House Gas trade H. Egelyng: Certified Organic Agriclture: Policy Instrument for Sustainable Development? M.S. Carter & N. Chirinda: No effect of cropping system on the greenhouse gas N2O J.H. Ingemann: Economics, Policy, and Organic Agriculture Brief news: TP ORganics needs you!, New publication: The World of ORganic Agriculture: Statistics and emerging trends, NJF seminar, Organic farmers bite back!, International conference on organic agriculture in Scope of environmental problems, Expo - MENOPE: 7th Middle East Natural and Organic Product

Memory CD4 T cell subsets are kinetically heterogeneous and replenished from naive T cells at high levels

Characterising the longevity of immunological memory requires establishing the rules underlying the renewal and death of peripheral T cells. However, we lack knowledge of the population structure and how self-renewal and de novo influx contribute to maintenance of memory compartments. Here, we characterise the kinetics and structure of murine CD4 T cell memory subsets by measuring the rates of influx of new cells and using detailed timecourses of DNA labelling that also distinguish the behaviour of recently divided and quiescent cells. We find that both effector and central memory CD4 T cells comprise subpopulations with highly divergent rates of turnover, and show that inflows of new cells sourced from the naive pool strongly impact estimates of memory cell lifetimes and division rates. We also demonstrate that the maintenance of CD4 T cell memory subsets in healthy mice is unexpectedly and strikingly reliant on this replenishment

Cattle farmers' preferences for disease-free zones in Kenya: an application of the choice experiment method

Management of livestock diseases is important in ensuring food safety to consumers in both domestic and export markets. Various measures are prescribed under the Sanitary and Phytosanitary Standards (SPS) agreement of the World Trade Organization. In order to prevent the spread of trans-boundary cattle diseases, the SPS agreement recommends the establishment of Disease-Free Zones (DFZs). These have been implemented successfully in some major beef-exporting countries, but in Kenya are still at a pilot stage. To understand Kenyan farmers' preferences on the type of DFZ that would be readily acceptable to them, a choice experiment was conducted using a D-optimal design. Results show that farmers would be willing to pay to participate in a DFZ where: adequate training is provided on pasture development, record keeping and disease monitoring; market information is provided and sales contract opportunities are guaranteed; cattle are properly labelled for ease of identification; and some monetary compensation is provided in the event that cattle die due to severe disease outbreaks. Preferences for the DFZ attributes are shown to be heterogeneous across three cattle production systems. We also derive farmers' preferences for various DFZ policy scenarios. The findings have important implications for policy on the design of DFZ programmes in Kenya and other countries that face similar cattle disease challenges. © 2011 Blackwell Publishing Ltd

Beyond Disagreement-based Agnostic Active Learning

We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, which has a high label requirement, and {\em{margin-based active learning}}, which only applies to fairly restricted settings. A major challenge is to find an algorithm which achieves better label complexity, is consistent in an agnostic setting, and applies to general classification problems. In this paper, we provide such an algorithm. Our solution is based on two novel contributions -- a reduction from consistent active learning to confidence-rated prediction with guaranteed error, and a novel confidence-rated predictor

Taking the bite out of automated naming of characters in TV video

We investigate the problem of automatically labelling appearances of characters in TV or film material with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”