Sample Size Estimation using the Receiver Operating Characteristic Curve

In this paper we describe two related approaches to estimating the sample sizes required to statistically compare the performance of two classifiers: acceptable failure rates (AFR) and the area under the receiver operating characteristic (ROC) curve (AUC). In particular, we consider rare event detection problems, where the prior class probabilities are highly skewed, and measure performance at a specific operating point and for the whole ROC curve. It is shown that the use of AUC as a performance measure is preferable to AFR as it requires a smaller data set to demonstrate superiority of one classifier over another

Tensor products of subspace lattices and rank one density

We show that, if $M$ is a subspace lattice with the property that the rank one subspace of its operator algebra is weak* dense, $L$ is a commutative subspace lattice and $P$ is the lattice of all projections on a separable infinite dimensional Hilbert space, then the lattice $L\otimes M\otimes P$ is reflexive. If $M$ is moreover an atomic Boolean subspace lattice while $L$ is any subspace lattice, we provide a concrete lattice theoretic description of $L\otimes M$ in terms of projection valued functions defined on the set of atoms of $M$. As a consequence, we show that the Lattice Tensor Product Formula holds for \Alg M and any other reflexive operator algebra and give several further corollaries of these results.Comment: 15 page

High resolution 3-D imaging via multi-pass SAR

Spaceborne/airborne synthetic aperture radar (SAR) systems provide high resolution two-dimensional terrain imagery. The paper proposes a technique for combining multiple SAR images, acquired on flight paths slightly separated in the elevation direction, to generate high resolution three-dimensional imagery. The technique could be viewed as an extension to interferometric SAR (InSAR) in that it generates topographic imagery with an additional dimension of resolution. The 3-D multi-pass SAR imaging system is typically characterised by a relatively short ambiguity length in the elevation direction. To minimise the associated ambiguities we exploit the relative phase information within the set of images to track the terrain landscape. The SAR images are then coherently combined, via a nonuniform DFT, over a narrow (in elevation) volume centred on the 'dominant' terrain ground plane. The paper includes a detailed description of the technique, background theory, including achievable resolution, and the results of an experimental stud

Mutiprocessor Adaptation of a Texture Segmentation Scheme for Satellite Radar Images

This paper presents a scheme for segmenting images on the basis of differences in localised measures of spatial texture. The scheme used was originally proposed by Wilson and Spann [1] but incorporates a new clustering algorithm which gives improved overall segmentation performance. The Wilson and Spann [1] algorithm uses a clustering algorithm which proved susceptible to initial input parameters and gave poor segmentation on our images. Our algorithm uses a modification of the Koontz, Narendra and Fukunaga [2] clustering algorithm. By linking the clustering to the resolution of the image, significant clusters were able to be realised, yielding a more robust segmentation scheme. The adaptation also resulted in a significant reduction in run-time. The paper is directed towards the problem of segmenting satellite synthetic aperture radar (SAR) images and we give comparisons of the techniques on SAR and other images

Texture Classification Using Nonparametric Random Fields

We present a nonparametric Markov Random Field model for classifying texture images. This model can capture the characteristics of a wide variety of textures, varying from the highly structured to the stochastic. The power of our modelling technique is evident in that only a small training image is required, even when the training texture contains long range characteristics. We show how this model can be used for unsupervised segmentation and classification of images containing textures for which we have no prior knowledge of the constituent texture types. This technique can therefore be used to find a specific texture in a background of unknown textures

Classification Of Cervical Cell Nuclei Using Morphological Segmentation And Texture Feature Extraction

This paper presents preliminary results for the classification of Pap smear cell nuclei, using Gray Level Co-occurrence Matrix (GLCM) textual features. We outline a method of nuclear segmentation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modified form of the GLCM are extracted over several angle and distance measures. Linear Discriminant Analysis is preformed on these features to reduce the dimensionality of the feature space, and a classifier with hyper quadric decision surface is implemented to classify a small set of normal and abnormal cell nuclei. Using 2 features, we achieve a misclassification rate of 3.3% on a data set of 61 cells

Improved Machine Tool Linear Axis Calibration Through Continuous Motion Data Capture

Machine tool calibration is becoming recognised as an important part of the manufacturing process. The current international standards for machine tool linear axes calibration support the use of quasi-static calibration techniques. These techniques can be time consuming but more importantly a compromise in quality due to the practical restriction on the spatial resolution of target positions on the axis under test. Continuous motion calibration techniques have the potential to dramatically increase calibration quality. Through taking several measurement values per second while the axis under test is in motion, it is possible to measure in far greater detail. Furthermore, since machine tools normally operate in dynamic mode, the calibration data can be more representative if it is captured while the machine is in motion. The drawback to measuring the axis while in motion is the potential increase in measurement uncertainty. In the following paper, different methods of continuous motion calibration are discussed. A time-based continuous motion solution is proposed as well as a novel optimisation and correlation algorithm to accurately fuse the data taken from quasi-static and continuous motion measurements. The measurement method allows for minimal quasi-static measurements to be taken while using a continuous motion measurement to enhance the calibration process with virtually no additional time constraints. The proposed method does not require any additional machine interfacing, making it a more readily accessible solution for widespread machine tool use than other techniques which require hardware links to the CNC. The result of which means a shorter calibration routine and enhanced results. The quasi-static and continuous motion measurements showed correlation to within one micrometre at the quasi-static measurement targets. An error of 13 μm was detailed on the continuous motion, but was missed using the standard test. On a larger, less accurate machine, the quasi-static and continuous motion measurements were on average within 3 μm of each other however, showed a standard deviation of 4 μm which is less than 1% of the overall error. Finally, a high frequency cyclic error was detected in the continuous motion measurement but was missed in the quasi-static measuremen

Monetary Policy, Bank Bailouts and the Sovereign-Bank Risk Nexus in the Euro Area

The paper analyses the empirical relationship between bank risk and sovereign credit risk in the euro area. Using structural VAR with daily financial markets data for 2003-13, the analysis confirms two-way causality between shocks to sovereign risk and bank risk, with the former being overall more important in explaining bank risk, than vice versa. The paper focuses specifically on the impact of non-standard monetary policy measures by the European Central Bank and on the effects of bank bailout policies by national governments. Testing specific hypotheses formulated in the literature, we find that bank bailout policies have reduced solvency risk in the banking sector mostly at the expense of raising the credit risk of sovereigns. By contrast, monetary policy was in most, but not all cases effective in lowering credit risk among both sovereigns and banks. Finally, we find spillover effects in particular from sovereigns in the euro area periphery to the core countries

Functional cyclophilin D moderates platelet adhesion, but enhances the lytic resistance of fibrin

In the course of thrombosis, platelets are exposed to a variety of activating stimuli classified as ‘strong’ (e.g. thrombin and collagen) or ‘mild’ (e.g. ADP). In response, activated platelets adhere to injured vasculature, aggregate, and stabilise the three-dimensional fibrin scaffold of the expanding thrombus. Since ‘strong’ stimuli also induce opening of the mitochondrial permeability transition pore (MPTP) in platelets, the MPTP-enhancer Cyclophilin D (CypD) has been suggested as a critical pharmacological target to influence thrombosis. However, it is poorly understood what role CypD plays in the platelet response to ‘mild’ stimuli which act independently of MPTP. Furthermore, it is unknown how CypD influences platelet-driven clot stabilisation against enzymatic breakdown (fibrinolysis). Here we show that treatment of human platelets with Cyclosporine A (a cyclophilin-inhibitor) boosts ADP-induced adhesion and aggregation, while genetic ablation of CypD in murine platelets enhances adhesion but not aggregation. We also report that platelets lacking CypD preserve their integrity in a fibrin environment, and lose their ability to render clots resistant against fibrinolysis. Our results indicate that CypD has opposing haemostatic roles depending on the stimulus and stage of platelet activation, warranting a careful design of any antithrombotic strategy targeting CypD