SOME CONCEPTUAL PROBLEMS IN THE EVALUATION OF WATER POLLUTION DAMAGES

Environmental Economics and Policy,

Automating the Hunt for Volcanoes on Venus

Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern recognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers

THE WELFARE REFORM BILL AND ITS EFFECTS IN THE SOUTH

Political Economy,

Automated analysis of radar imagery of Venus: handling lack of ground truth

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the “ground truth” is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in “ground truth” is essential in applications of this nature

Entropy-based active learning for object recognition

Most methods for learning object categories require large amounts of labeled training data. However, obtaining such data can be a difficult and time-consuming endeavor. We have developed a novel, entropy-based ldquoactive learningrdquo approach which makes significant progress towards this problem. The main idea is to sequentially acquire labeled data by presenting an oracle (the user) with unlabeled images that will be particularly informative when labeled. Active learning adaptively prioritizes the order in which the training examples are acquired, which, as shown by our experiments, can significantly reduce the overall number of training examples required to reach near-optimal performance. At first glance this may seem counter-intuitive: how can the algorithm know whether a group of unlabeled images will be informative, when, by definition, there is no label directly associated with any of the images? Our approach is based on choosing an image to label that maximizes the expected amount of information we gain about the set of unlabeled images. The technique is demonstrated in several contexts, including improving the efficiency of Web image-search queries and open-world visual learning by an autonomous agent. Experiments on a large set of 140 visual object categories taken directly from text-based Web image searches show that our technique can provide large improvements (up to 10 x reduction in the number of training examples needed) over baseline techniques

Review article: Ever decreasing circles

Review of P.J. ASHMORE, Calanais: survey and excavation 1979-88, and RICHARD BRADLEY & COURTNEY NIMURA (eds), The use and reuse of stone circles: fieldwork at five Scottish monuments and its implications

THE ROLE OF ALTERNATIVE AGRICULTURAL ENTERPRISES IN A CHANGING AGRICULTURAL ECONOMY

Agribusiness,

Finding Faces in Cluttered Scenes using Random Labeled Graph Matching

An algorithm for locating quasi-frontal views of human faces in cluttered scenes is presented. The algorithm works by coupling a set of local feature detectors with a statistical model of the mutual distances between facial features it is invariant with respect to translation, rotation (in the plane), and scale and can handle partial occlusions of the face. On a challenging database with complicated and varied backgrounds, the algorithm achieved a correct localization rate of 95% in images where the face appeared quasi-frontally

Rock Segmentation through Edge Regrouping

Rockster is an algorithm that automatically identifies the locations and boundaries of rocks imaged by the rover hazard cameras (hazcams), navigation cameras (navcams), or panoramic cameras (pancams). The software uses edge detection and edge regrouping to identify closed contours that separate the rocks from the background

Vitamin D in Early Childhood and the Effect on Immunity to Mycobacterium tuberculosis

A potential role for vitamin D as a therapeutic immunomodulator in tuberculosis (TB) has been recognised for over 150 years, but has only recently returned to the centre of the research arena due to the increasing awareness of the global vitamin D deficiency epidemic. As early as birth a child is often deficient in vitamin D, which may not only affect their bone metabolism but also modulate their immune function, contributing to the increased susceptibility to many infections seen early in life. Recent studies have begun to explain the mechanisms by which vitamin D affects immunity. Antimicrobial peptides are induced in conjunction with stimulation of innate pattern recognition receptors enhancing immunity to particular infections. In contrast the role of vitamin D within the adaptive immune response appears to be more regulatory in function, perhaps as a mechanism to reduce unwanted inflammation. In this paper we focus on the effect of vitamin D on immunity to TB. Where much of the attention has been paid by past reviews to the role of vitamin D in adult TB patients, this paper, where possible, focuses on research in paediatric populations