The Three-Dimensional Interpretation of a Class of Simple Line-Drawings

We provide a theory of the three-dimensional interpretation of a class of line-drawings called p-images, which are interpreted by the human vision system as parallelepipeds ("boxes"). Despite their simplicity, p-images raise a number of interesting vision questions: *Why are p-images seen as three-dimensional objects? Why not just as flatimages? *What are the dimensions and pose of the perceived objects? *Why are some p-images interpreted as rectangular boxes, while others are seen as skewed, even though there is no obvious distinction between the images? *When p-images are rotated in three dimensions, why are the image-sequences perceived as distorting objects---even though structure-from-motion would predict that rigid objects would be seen? *Why are some three-dimensional parallelepipeds seen as radically different when viewed from different viewpoints? We show that these and related questions can be answered with the help of a single mathematical result and an associated perceptual principle. An interesting special case arises when there are right angles in the p-image. This case represents a singularity in the equations and is mystifying from the vision point of view. It would seem that (at least in this case) the vision system does not follow the ordinary rules of geometry but operates in accordance with other (and as yet unknown) principles.

Exploring Vision-Based Interfaces: How to Use Your Head in Dual Pointing Tasks

The utility of vision-based face tracking for dual pointing tasks is evaluated. We first describe a 3-D face tracking technique based on real-time parametric motion-stereo, which is non-invasive, robust, and self-initialized. The tracker provides a real-time estimate of a ?frontal face ray? whose intersection with the display surface plane is used as a second stream of input for scrolling or pointing, in paral-lel with hand input. We evaluated the performance of com-bined head/hand input on a box selection and coloring task: users selected boxes with one pointer and colors with a second pointer, or performed both tasks with a single pointer. We found that performance with head and one hand was intermediate between single hand performance and dual hand performance. Our results are consistent with previously reported dual hand conflict in symmetric pointing tasks, and suggest that a head-based input stream should be used for asymmetric control.

Fuzzy ART

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP synthesize fuzzy logic and ART networks by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic: intersection (∩) with the fuzzy intersection (∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric: theory in which the fuzzy inter:>ec:tion and the fuzzy union (∨), or component-wise maximum, play complementary roles. Complement coding preserves individual feature amplitudes while normalizing the input vector, and prevents a potential category proliferation problem. Adaptive weights :otart equal to one and can only decrease in time. A geometric interpretation of fuzzy AHT represents each category as a box that increases in size as weights decrease. A matching criterion controls search, determining how close an input and a learned representation must be for a category to accept the input as a new exemplar. A vigilance parameter (p) sets the matching criterion and determines how finely or coarsely an ART system will partition inputs. High vigilance creates fine categories, represented by small boxes. Learning stops when boxes cover the input space. With fast learning, fixed vigilance, and an arbitrary input set, learning stabilizes after just one presentation of each input. A fast-commit slow-recode option allows rapid learning of rare events yet buffers memories against recoding by noisy inputs. Fuzzy ARTMAP unites two fuzzy ART networks to solve supervised learning and prediction problems. A Minimax Learning Rule controls ARTMAP category structure, conjointly minimizing predictive error and maximizing code compression. Low vigilance maximizes compression but may therefore cause very different inputs to make the same prediction. When this coarse grouping strategy causes a predictive error, an internal match tracking control process increases vigilance just enough to correct the error. ARTMAP automatically constructs a minimal number of recognition categories, or "hidden units," to meet accuracy criteria. An ARTMAP voting strategy improves prediction by training the system several times using different orderings of the input set. Voting assigns confidence estimates to competing predictions given small, noisy, or incomplete training sets. ARPA benchmark simulations illustrate fuzzy ARTMAP dynamics. The chapter also compares fuzzy ARTMAP to Salzberg's Nested Generalized Exemplar (NGE) and to Simpson's Fuzzy Min-Max Classifier (FMMC); and concludes with a summary of ART and ARTMAP applications.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.

The AASK Database V3.0: a database of human experience during aircraft evacuation incidents

This paper describes recent developments with the Aircraft Accident Statistics and Knowledge (AASK) database. The AASK database is a repository of survivor accounts from aviation accidents developed by the Fire Safety Engineering Group of the University of Greenwich with support from the UK CAA. Its main purpose is to store observational and anecdotal data from the actual interviews of the occupants involved in aircraft accidents. Access to the latest version of the database (AASK V3.0) is available over the Internet. AASK consists of information derived from both passenger and cabin crew interviews, information concerning fatalities and basic accident details. Also provided with AASK is the Seat Plan Viewer that graphically displays the starting locations of all the passengers - both survivors and fatalities - as well as the exits used by the survivors. Data entered into the AASK database is extracted from the transcripts supplied by the National Transportation Safety Board in the US and the Air Accident Investigation Branch in the UK. The quality and quantity of the data was very variable ranging from short summary reports of the accidents to boxes of individual accounts from passengers, crew and investigators. Data imported into AASK V3.0 includes information from 55 accidents and individual accounts from 1295 passengers and 110 crew.

Modeling marine ecosystem responses to global climate change: Where are we now and where should we be going?

Modeling of global climate change is moving from global circulation model (GCM)-type projections with coupled biogeochemical models to projections of ecological responses, including food web and upper trophic levels. Marine and coastal ecosystems are highly susceptible to the impacts of global climate change and also produce significant ecosystem services. The effects of global climate change on coastal and marine ecosystems involve a much wider array of effects than the usual temperature, sea level rise, and precipitation. This paper is an overview for a collection of 12 papers that examined various aspects of global climate change on marine ecosystems and comprise this special issue. We summarized the major features of the models and analyses in the papers to determine general patterns. A wide range of ecosystems were simulated using a diverse set of modeling approaches. Models were either 3-dimensional or used a few spatial boxes, and responses to global climate change were mostly expressed as changes from a baseline condition. Three issues were identified from the across-model comparison: (a) lack of standardization of climate change scenarios, (b) the prevalence of site-specific and even unique models for upper trophic levels, and (c) emphasis on hypothesis evaluation versus forecasting. We discuss why these issues are important as global climate change assessment continues to progress up the food chain, and, when possible, offer some initial steps for going forward.

European Project on Ocean Acidification (EPOCA): Objectives, products and scientific highlights

The European Project on Ocean Acidification (EPOCA) is Europe's first large-scale research initiative devoted to studying the impacts and consequences of ocean acidification. More than 100 scientists from 27 institutes and nine countries bring their expertise to the project, resulting in a multidisciplinary and versatile consortium. The project is funded for four years (2008 to 2012) by the European Commission within its Seventh Framework Programme. This article describes EPOCA and explains its different aspects, objectives, and products. Following a general introduction, six boxes highlight outcomes, techniques, and scientific results from each of the project's core themes.

Effect of chronic angiotensin converting enzyme inhibition on spatial memory and anxiety-like behavours in rats

Angiotensin converting enzyme inhibitors (ACEis) are widely used anti-hypertensive agents that are also reported to have positive effects on mood and cognition. The present study examined the influence of the ACEi, perindopril, on cognitive performance and anxiety measures in rats. Two groups of rats were treated orally for one week with the ACEi, perindopril, at doses of 0.1 and 1.0mg/kg/day. Learning was assessed by the reference memory task in the water maze, comparing treated to control rats. Over five training days both perindopril-treated groups learnt the location of the submerged platform in the water maze task significantly faster than control rats. A 60s probe trial on day 6 showed that the 1.0mg/kg/day group spent significantly longer time in the training quadrant than control rats. This improved performance in the swim maze task was not due to the effect of perindopril on motor activity or the anxiety levels of the rats as perindopril-treated and control animals behaved similarly in activity boxes and on the elevated+maze. These results confirm the anecdotal human studies that ACEis have a positive influence on cognition and provide possibilities for ACEis to be developed into therapies for memory loss.

Color preferences in gorillas (Gorilla gorilla gorilla) and chimpanzees (Pan troglodytes)

Color plays an important biological role in the lives of many animals, with some species exhibiting preferences for certain colors over others. This study explored the color preferences of two species of ape, which, like humans, possess trichromatic color vision. Six western lowland gorillas, and six chimpanzees, housed in Belfast Zoological Gardens, were exposed to three stimuli (cloths, boxes, sheets of acetate) in red, blue, and green. Six stimuli of the same nature, in each of the three colors, were provided to both species for 5 days per stimulus. The amount of interest that the animals showed toward each stimulus of each color was recorded for 1 hr. Results showed that the apes, both when analyzed as two separate groups, and when assessed collectively, showed significant color preferences, paying significantly less attention to the red-, than to the blue- or green-colored stimuli. The animals' interest in the blue- and green-colored stimuli did not differ significantly. Overall, the findings suggest that gorillas and chimpanzees, our closest living relatives, may harbor color preferences comparable to those of humans and other species. © 2008 American Psychological Association.


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A survey of sidedness in Northern Irish schoolchildren: The interaction of sex, age, and task

The study examined lateral preference in use of hands, feet, eyes, and ears in a group of nearly 5000 schoolchildren in Northern Ireland. Performance tests were carried out by student teachers during their school-based work in 2002 and data were submitted on-line. Six tasks were used-writing, throwing a ball, kicking a ball, hopping, listening to quiet sounds, and looking through a cardboard tube. There was right bias in every task but the extent of it differed between tasks. Males were generally less right biased than females, and younger children less than older ones; for hearing, the changes with age were markedly different in the two sexes, with females showing a strong increase in right bias but males showing none. These observational results do little to illuminate the reasons for the patterns observed.

The development of regret

In two experiments, 4- to 9-year-olds played a game in which they
selected one of two boxes to win a prize. On regret trials the unchosen
box contained a better prize than the prize children actually
won, and on baseline trials the other box contained a prize of the
same value. Children rated their feelings about their prize before
and after seeing what they could have won if they had chosen
the other box and were asked to provide an explanation if their
feelings had changed. Patterns of responding suggested that regret
was experienced by 6 or 7 years of age; children of this age could
also explain why they felt worse in regret trials by referring to
the counterfactual situation in which the prize was better. No evidence
of regret was found in 4- and 5-year-olds. Additional findings
suggested that by 6 or 7 years, children’s emotions were
determined by a consideration of two different counterfactual
scenarios.

Relationships between lameness and feeding behaviour in cubicle-housed Holstein-Friesian dairy cows

The relationship between lameness and feeding behaviour in dairy cows is not yet fully understood. This study examined the effect of lameness on feeding behaviour at two points during lactation. Forty-five Holstein–Friesian dairy cows (average parity 3.3) were housed in cubicle accommodation after calving and fed a total mixed ration (TMR). At approximately 60 and 120 days post partum, 48 h of information on feeding behaviour (including number of meals eaten, meal duration, meal size and feeding rate) was collected for each animal using feed boxes fitted to a data recording system. At the same time points, locomotion scores were recorded for each cow as a measure of lameness (1.0-sound to 4.5-severely lame). Relationships between feeding behaviour and locomotion score were analysed using Residual Maximum Likelihood (REML) analysis. At both time points, cows with higher locomotion scores ate fewer (P < 0.001), larger meals (P < 0.001) and had a shorter total feeding time (P < 0.001). At day 60 post partum, an increase in locomotion score was associated with a decrease in dry matter intake (P < 0.05), but at day 120 post partum no relationship was found between locomotion score and DMI. No relationship was found at either time point between locomotion score and mean meal duration or rate of feeding. The results of this study suggest that the effect of lameness on feeding behaviour in dairy cows does not remain constant across lactation.

Risk-informed decision-making in the presence of epistemic uncertainty

An important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we, therefore, propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decisionmaker’s attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process.

Transcriptional regulation of a brown adipocyte-specific gene, UCP1, by KLF11 and KLF15

Several growth factors and transcription factors have been reported to play important roles in brown adipocyte differentiation and modulation of thermogenic gene expression, especially the expression of UCP1. In this study, we focused on KLF11 and KLF15, which were expressed highly in brown adipose tissue. Our data demonstrated that KLF11 and KLF15 interacted directly with the UCP1 promoter using GC-box and GT-boxes, respectively. Co-transfection of KLF11 and KLF15 in the mesenchymal stem cell line muBM3.1 during brown adipocyte differentiation enhanced the expression level of UCP1. KLF11, but not KLF15, was essential for UCP1 expression during brown adipocyte differentiation of muBM3.1.