Getting Constitutional Theory into Proportion: A Matter of Interpretation?

Gibbs, N., Getting Constitutional Theory into Proportion: A Matter of Interpretation?, Oxford Journal of Legal Studies, 27 (1), 175-191. RAE2008

Why Ideas Matter in International Relations : Hans Morgenthau, Classical Realism, and the Moral Construction of Power Politics

Williams, Mike, 'Why ideas matter in International Relations: Hans Morgenthau, Classical Realism, and the Moral Construction of Power Politics', International Organization (2004) 58(4) pp.633-665 RAE2008

Reinventing the Matter of Britain: Undermining the State in Jacobean Masques

Archer, Jayne, Berry, Philippa, 'Reinventing the Matter of Britain: Undermining the State in Jacobean Masques', In: British Identities and English Renaissance Literature, David J. Baker and Willy Maley (eds),(Cambridge: Cambridge University Press), pp.119-134, 2002 RAE2008

Diurnal variation in uptake and xylem contents of inorganic and assimilated N under continuous and interrupted N supply to Phleum pratense and Festuca pratensis

J. H. Macduff and A. K. Bakken. (2003). Diurnal variation in uptake and xylem contents of inorganic and assimilated N under continuous and interrupted N supply to Phleum pratense and Festuca pratensis. Journal of Experimental Botany, 54 (381) pp.431-444 Sponsorship: BBSRC / Norwegian Crop Research Institute RAE2008

Competitive relationships in a fertile grassland community - does size matter?

John Warren, Fred Wilson & Anita Diaz (2002). Competitive relationships in a fertile grassland community - does size matter? Oecologia, 132 (1) pp.125-130 Sponsorship: SEERAD / Leverhulme Trust RAE2008

Tectonics Inversions & Emerald Deposits

In spite of the great amount of emerald deposits throughout the world, the priorities in quality and volume of extracted rough material are the sites of Colombia (Muzo and Chivor emerald belts). This sites are know even before the Spanish conquistadores. Emeralds were extracted from Somondoco mine (today Chivor) since 1537 and from Muzo in 1567. Contrariwise to the majority of the emerald deposits of the world, which are associated with granitic rocks, the Colombian emerald deposits are associated with hydrofracturing (the main factor controlling emerald mineralization) and hydrothermal fluids, rich in beryl, chrome and vanadium, induced by a tectonic inversion of the deep Mesozoic backarc basin, which is also responsible of the majority of the petroleum systems of the foredeep and foldbelt areas (maturation of the source-rocks andcreation of structural traps). The host rocks of the emeralds are carbonaceous calsiltites (calcareous schists) rich in organic matter of Lower Cretaceous age, which are cut by calcite veins, which, often, contain emeralds, particularly when they are folded. Indeed, since long time (Cheilletz, A. and Giulliani, G., 1996) suggested a two-stage model for the formation of the Colombian emeralds : (i) Stage I is characterized by décollement planes (early compressional tectonic regime) within the carbonaceous calsiltites, hydrothermal fluid infiltration and wall-rock metasomatic alteration ; (ii) Stage II (late tectonic regime) deforms the previous veins by thrust-related folds (development of stratiform and hydraulic breccia), which are synchronous of the emerald mineralization. The resulting tectonic structures are complex fold patterns characterized by propagation anticlines with emerald veins and emerald hydraulic breccia in the apexes, as in Quipama, Tendenquema and Chivor mines. Otherwise stated, since all emerald exploitations are, presently underground, exhaustive geological and particularly structural studies are required to reduce the probability of disappointments. The color of emeralds is from light green to thick green with obvious pleochroism. They appears with different colors when observed at different angles, especially with polarized light. The emeralds from Coscuez deposits have a homogeneous intensive color and bluish tone. At Muzo deposit, the emeralds have middle or dark green color with yellowish tone. At the Chivor deposits, the emeralds have less intensive green color with slight bluish tone. The typical inclusions are albite and pyrite, as well as long bubbles with three phase-inclusions according the zones of growth and along the crystal shapes.

Parameter Sensitive Detectors

Object detection can be challenging when the object class exhibits large variations. One commonly-used strategy is to first partition the space of possible object variations and then train separate classifiers for each portion. However, with continuous spaces the partitions tend to be arbitrary since there are no natural boundaries (for example, consider the continuous range of human body poses). In this paper, a new formulation is proposed, where the detectors themselves are associated with continuous parameters, and reside in a parameterized function space. There are two advantages of this strategy. First, a-priori partitioning of the parameter space is not needed; the detectors themselves are in a parameterized space. Second, the underlying parameters for object variations can be learned from training data in an unsupervised manner. In profile face detection experiments, at a fixed false alarm number of 90, our method attains a detection rate of 75% vs. 70% for the method of Viola-Jones. In hand shape detection, at a false positive rate of 0.1%, our method achieves a detection rate of 99.5% vs. 98% for partition based methods. In pedestrian detection, our method reduces the miss detection rate by a factor of three at a false positive rate of 1%, compared with the method of Dalal-Triggs.

Learning a Family of Detectors

Object detection and recognition are important problems in computer vision. The challenges of these problems come from the presence of noise, background clutter, large within class variations of the object class and limited training data. In addition, the computational complexity in the recognition process is also a concern in practice. In this thesis, we propose one approach to handle the problem of detecting an object class that exhibits large within-class variations, and a second approach to speed up the classification processes. In the first approach, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved with using a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. For applications where explicit parameterization of the within-class states is unavailable, a nonparametric formulation of the kernel can be constructed with a proper foreground distance/similarity measure. Detector training is accomplished via standard Support Vector Machine learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the image masks for foreground objects are provided in training, the detectors can also produce object segmentation. Methods for generating a representative sample set of detectors are proposed that can enable efficient detection and tracking. In addition, because individual detectors verify hypotheses of foreground state, they can also be incorporated in a tracking-by-detection frame work to recover foreground state in image sequences. To run the detectors efficiently at the online stage, an input-sensitive speedup strategy is proposed to select the most relevant detectors quickly. The proposed approach is tested on data sets of human hands, vehicles and human faces. On all data sets, the proposed approach achieves improved detection accuracy over the best competing approaches. In the second part of the thesis, we formulate a filter-and-refine scheme to speed up recognition processes. The binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the face recognition grand challenge version 2 data set, hand shape detection and parameter estimation on a hand data set, and vehicle detection and estimation of the view angle on a multi-pose vehicle data set. On all data sets, our approach is at least five times faster than simply evaluating all foreground state hypotheses with virtually no loss in classification accuracy.