Distribution matching for transduction

Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.

Structured learning for information retrieval

Information retrieval is the area of study concerned with the process of searching, recovering and interpreting information from large amounts of data. In this Thesis we show that many of the problems in information retrieval consist of structured learning, where the goal is to learn predictors of complex output structures, consisting of many inter-dependent variables. We then attack these problems using principled machine learning methods that are specifically suited for such scenarios. In the process of doing so, we develop new models, new model extensions and new algorithms that, when integrated with existing methodology, comprise a new set of tools for solving a variety of information retrieval problems. Firstly, we cover the multi-label classification problem, where we seek to predict a set of labels associated with a given object; the output in this case is structured, as the output variables are interdependent. Secondly, we focus on document ranking, where given a query and a set of documents associated with it we want to rank them according to their relevance with respect to the query; here, again, we have a structured output - a ranking of documents. Thirdly, we address topic models, where we are given a set of documents and attempt to find a compact representation of them, by learning latent topics and associating a topic distribution to each document; the output is again structured, consisting of word and topic distributions. For all the above problems, we obtain state-of-the-art solutions as attested by empirical performance in publicly available real-world datasets

Dominique Fourcade: Recalculations

This article explores the relationship between poetry, photography and choreography in the writings of contemporary French poet Dominique Fourcade. Close readings of Fourcade’s 2001 M W alongside other of his works reveal some of the fundamental techniques of his writing, founded on formal recalculations sparked by interaction with other art forms, specifically the photography of Barbara Morgan and Isabelle Waternaux and the dance choreography of George Balanchine, Merce Cunningham, Pina Bausch, Mathilde Monnier and William Forsythe. The analysis also proposes alternative multi-disciplinary directions for the reception of poetry

Submodular Multi-Label Learning

In this paper we present an algorithm to learn a multi-label classifier which attempts at directly optimising the F-score. The key novelty of our for-mulation is that we explicitly allow for assortative (submodular) pairwise label interactions, i.e., we can leverage the co-ocurrence of pairs of labels in order to improve the quality of prediction. Prediction in this model consists of minimising a particular submodular set function, what can be accomplished exactly and efficiently via graph-cuts. Learning however is substantially more involved and requires the solution of an intractable com-binatorial optimisation problem. We present an approximate algorithm for this problem and prove that it is sound in the sense that it never predicts incorrect labels. We also present a nontrivial test of a sufficient condition for our algorithm to have found an optimal solution. We present exper-iments on benchmark multi-label datasets, which attest the value of the proposed technique. We also make available source code that enables the reproduction of our experiments.

Multitask learning without label correspondences

We propose an algorithm to perform multitask learning where each task has potentially distinct label sets and label correspondences are not readily available. This is in contrast with existing methods which either assume that the label sets shared by different tasks are the same or that there exists a label mapping oracle. Our method directly maximizes the mutual information among the labels, and we show that the resulting objective function can be efficiently optimized using existing algorithms. Our proposed approach has a direct application for data integration with different label spaces for the purpose of classification, such as integrating Yahoo! and DMOZ web directories

Management and Considerations in the Usage of TXA in Hemorrhagic Trauma Patients

Tranexamic acid is a synthetic reversible competitive inhibitor that exerts its action on plasminogen, preventing plasmin formation and deterring fibrinolysis.1 Current FDA-approved indications of TXA include heavy menstrual bleeding and short-term blood loss prevention in patients with hemophilia following tooth extraction.1 TXA has more recently been utilized in the management of massive hemorrhagic trauma patients despite this being an off-label use. While TXA has shown promise as a hemostatic agent for this patient population, considerations in the pre-hospital and hospital settings must be examined for its integration into massive hemorrhage protocols

Crustal evolution of the Saykhandulaan inlier, Mongolia : implications for Palaeozoic arc magmatism, polyphase deformation and terrane accretion in the southeast Gobi mineral belt

The Saykhandulaan Inlier in South East Mongolia lies within the Central Asian Orogenic Belt (CAOB), and records a complex history of Palaeozoic tectonism and magmatism associated with terrane accretion on the northern margin of the Palaeo-Asian ocean. The inlier spans the boundary between the Gobi Altai back-arc basin terrane in the north and the Mandalovoo and Gurvansayhan island-arc terranes in the south which are notable for their many mineralised intrusions, including the Oyu Tolgoi gold-rich copper porphyry deposit. Results from cross-strike transects within the Saykhandulaan Inlier reveal that it can be subdivided into five parallel east–west striking litho-tectonic domains; (1) the Northern Slate Belt, comprising Devonian greenschist grade pelites and psammites with deep-marine to coastal siliciclastic protoliths; (2) the Saykhandulaan Valley Lineament Zone (SVLZ), a tectonised zone of faulted and lithologically altered volcanic rocks; (3) the High Strain Belt, consisting of tightly folded and flattened metamorphosed clastic sedimentary rocks; (4) the Molasse Succession, consisting of relatively undeformed coarse conglomerates and sandstones and, (5) the Oyut Ulaan Volcanic Group, a nearly 5 km-thick folded Carboniferous volcanic succession that hosts the mid-Carboniferous Oyut Ulaan mineralised granite. The Northern Slate Belt metasedimentary rocks record a northerly cratonic provenance, whereas all rocks to the south of the SVLZ have arc affinities. The SVLZ is thus interpreted to be the boundary between the Gobi Altai and Mandalovoo terranes. Two major deformation events are documented; (1) back-arc basin closure and inversion involving regional scale folding and greenschist grade metamorphism in the Northern Slate and High Strain Belts; (2) contraction associated with Mandalovoo terrane accretion and final closure of the Palaeo-Asian Ocean to the south. Following terrane accretion and cessation of subduction, crustal extension and strike-slip faulting further modified the crustal architecture of the inlier. The results presented here provide a useful framework for understanding the crustal evolution of adjacent regions within the southeast Gobi mineral belt