Adaptive auditory risk assessment in the dogbane tiger moth when pursued by bats

Moths and butterflies flying in search of mates risk detection by numerous aerial predators; under the cover of night, the greatest threat will often be from insectivorous bats. During such encounters, the toxic dogbane tiger moth, Cycnia tenera uses the received intensity, duration and emission pattern of the bat's echolocation calls to determine when, and how many, defensive ultrasonic clicks to produce in return. These clicks, which constitute an acoustic startle response, act as warning signals against bats in flight. Using an integrated test of stimulus generalization and dishabituation, here we show that C. tenera is able to discriminate between the echolocation calls characteristic of a bat that has only just detected it versus those of a bat actively in pursuit of it. We also show that C. tenera habituates more profoundly to the former stimulus train (‘early attack’) than to the latter (‘late attack’), even though it was initially equally responsive to both stimuli. Matched sensory and behavioural data indicate that reduced responsiveness reflects habituation and is not merely attributable to sensory adaptation or motor fatigue. In search of mates in the face of bats, C. tenera's ability to discriminate between attacking bats representing different levels of risk, and to habituate less so to those most dangerous, should function as an adaptive cost–benefit trade-off mechanism in nature

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Localized Component Analysis for Arthritis Detection in the Trapeziometacarpal Joint

The trapeziometacarpal joint enables the prehensile function of the thumb. Unfortunately, this joint is vulnerable to osteoarthritis (OA) that typically affects the local shape of the trapezium. A novel, local statistical shape model is defined that employs a differentiable locality measure based on the weighted variance of point coordinates per mode. The simplicity of the function and the smooth derivative enable to quickly determine localized components for densely sampled surfaces. The method is employed to assess a set of 60 trapezia (38 healthy, 22 with OA). The localized components predominantly model regions affected by OA, contrary to shape variations found with PCA. Furthermore, identification of pathological trapezia based on the localized modes of variation is improved compared to PC

Multi-band Modelling of Appearance

Earlier work has demonstrated generative models capable of synthesising near photo-realistic grey-scale images of objects. These models have been augmented with colour information, and recently with edge information. This paper extends the Active Appearance Model framework by modelling the appearance of both derived feature bands and an intensity band. As a special case of featureband augmented appearance modelling we propose a dedicated representation with applications to face segmentation. The representation addresses a major problem within face recognition by lowering the sensitivity to lighting conditions. Results show that localisation accuracy of facial features is considerably increased using this appearance representation under diffuse and directional lighting and at multiple scales

Example based non-rigid shape detection

Abstract. Since it is hard to handcraft the prior knowledge in a shape detection framework, machine learning methods are preferred to exploit the expert annotation of the target shape in a database. In the previous approaches [1, 2], an optimal similarity transformation is exhaustively searched for to maximize the response of a trained classification model. At best, these approaches only give a rough estimate of the position of a non-rigid shape. In this paper, we propose a novel machine learning based approach to achieve a refined shape detection result. We train a model that has the largest response on a reference shape and a smaller response on other shapes. During shape detection, we search for an optimal nonrigid deformation to maximize the response of the trained model on the deformed image block. Since exhaustive searching is inapplicable for a non-rigid deformation space with a high dimension, currently, example based searching is used instead. Experiments on two applications, left ventricle endocardial border detection and facial feature detection, demonstrate the robustness of our approach. It outperforms the well-known ASM and AAM approaches on challenging samples.

Luottamuksen arviointi komponenttipohjaisessa ohjelmistoarkkitehtuurissa

Component-based software architecture enables extending the functionality of a system with additional software modules, called components. An open architecture makes it possible for software vendors to provide various components for the end-users. Installing third party software into a system introduces, however, threats that cannot be ignored. The threats are especially troubling in case of mobile and embedded devices since their faulty functioning may make them completely unusable or cause significant monetary loss. The growing importance of software in embedded systems, its economic value and the fact that individuals and societies depend more and more on the correct functioning of these embedded systems are the major motivations for developing secure component architecture. This thesis describes a trust model designed to be used in component-based software architecture. The architecture is developed for the needs of embedded devices, for which dependability is particularly important. The aim is that using the trust model, it is possible to evaluate the trustworthiness of the installed components and, furthermore, to maintain the correct operation of a system. This thesis also introduces a recommendation mechanism, which enables the devices to communicate their observations about the operation of a component to other devices. In this way, the devices gain valuable additional information about the trustworthiness of the component. The trust model with the recommendation mechanism was experimented with simulation. The model contains many parameters that relate to the details of the trustworthiness evaluation procedure. The simulation shows that the choice of parameters has a major effect on the results. In a situation where a component may operate either well or badly, some parameter sets are appropriate for maximizing the number of good experiences and others for minimizing the number of bad ones