A model of ant route navigation driven by scene familiarity

In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints

Chemical derivation to enhance the chemical/oxidative stability of resorcinol-formaldehyde (R-F) resin

Tank wastes at Hanford and SRS contain highly alkaline supernate solutions of conc. Na, K nitrates with large amounts of {sup 137}Cs. It is desirable to remove and concentrate the highly radioactive fraction for vitrification. One candidate ion exchange material for removing the radiocesium is R-F resin. This report summarizes studies into synthesis and characterization of 4-derivatized R-F resins prepared in pursuit of more chemically/oxidatively robust resin. 85% 4-fluororesorcinol/15% phenol formaldehyde resin appears to have good stability in alkaline solution, although there may be some nucleophilic displacement reaction during synthesis; further studies are needed

Domain wall roughening in dipolar films in the presence of disorder

We derive a low-energy Hamiltonian for the elastic energy of a N\'eel domain wall in a thin film with in-plane magnetization, where we consider the contribution of the long-range dipolar interaction beyond the quadratic approximation. We show that such a Hamiltonian is analogous to the Hamiltonian of a one-dimensional polaron in an external random potential. We use a replica variational method to compute the roughening exponent of the domain wall for the case of two-dimensional dipolar interactions.Comment: REVTEX, 35 pages, 2 figures. The text suffered minor changes and references 1,2 and 12 were added to conform with the referee's repor

Detection of antibacterial activity of essential oil components by TLC-bioautography using luminescent bacteria

The aim of the present study was the chemical characterization of some medically relevant essential oils (tea tree, clove, cinnamon bark, thyme and eucalyptus) and the investigation of antibacterial effect of the components of these oils by use of a direct bioautographic method. Thin layer chromatography (TLC) was combined with biological detection in this process. The chemical composition of the oils was determined by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). Eucalyptol (84.2%) was the main component of the essential oil of eucalyptus, eugenol (83.7%) of clove oil, and trans-cinnamic aldehyde (73.2%), thymol (49.9%) and terpinen-4-ol (45.8%) of cinnamon bark, thyme and tea tree oils, respectively. Antibacterial activity of the separated components of these oils, as well as their pure main components (eucalyptol, eugenol, trans-cinnamic aldehyde and thymol) was observed against the Gram-negative luminescence tagged plant pathogenic bacterium Pseudomonas syringae pv. maculicola (Psmlux) and the Gram-negative, naturally luminescent marine bacterium Vibrio fischeri. On the whole, the antibacterial activity of the essential oils could be related to their main components, but the minor constituents may be involved in this process. Trans-cinnamic aldehyde and eugenol were the most active compounds in TLC-bioautography. The sensitivity of TLC-bioautographic method can be improved with using luminescent test bacteria. This method is more cost-effective and provides more reliable results in comparison with conventional microbiological methods, e.g. disc-diffusion technique

Mean field dilute ferromagnet I. High temperature and zero temperature behavior

We study the mean field dilute model of a ferromagnet. We find and prove an expression for the free energy density at high temperature, and at temperature zero. We find the critical line of the model, separating the phase with zero magnetization from the phase with symmetry breaking. We also compute exactly the entropy at temperature zero, which is strictly positive. The physical behavior at temperature zero is very interesting and related to infinite dimensional percolation, and suggests possible behaviors at generic low temperatures. Lastly, we provide a complete solution for the annealed model. Our results hold both for the Poisson and the Bernoulli versions of the model.Comment: 38 page

Implicit Surface Modelling with a Globally Regularised Basis of Compact Support

We consider the problem of constructing a globally smooth analytic function that represents a surface implicitly by way of its zero set, given sample points with surface normal vectors. The contributions of the paper include a novel means of regularising multi-scale compactly supported basis functions that leads to the desirable interpolation properties previously only associated with fully supported bases. We also provide a regularisation framework for simpler and more direct treatment of surface normals, along with a corresponding generalisation of the representer theorem lying at the core of kernel-based machine learning methods. We demonstrate the techniques on 3D problems of up to 14 million data points, as well as 4D time series data and four-dimensional interpolation between three-dimensional shapes

Spatial Representation and Navigation in a Bio-inspired Robot

A biologically inspired computational model of rodent repre-sentation?based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information about the environment and consists of a large population of location-sensitive units (place cells) with overlapping receptive fields. Goal navigation is performed using reinforcement learning in continuous state and action spaces, where the state space is represented by population activity of the place cells. The model is able to reproduce a number of behavioral and neuro-physiological data on rodents. Performance of the model was tested on both simulated and real mobile Khepera robots in a set of behavioral tasks and is comparable to the performance of animals in similar tasks

Vision-based localization using a central catadioptric vision system

10.1007/978-3-540-77457-0_37Springer Tracts in Advanced Robotics39397-40

Plant Classification from Bat-Like Echolocation Signals

Classification of plants according to their echoes is an elementary component of bat behavior that plays an important role in spatial orientation and food acquisition. Vegetation echoes are, however, highly complex stochastic signals: from an acoustical point of view, a plant can be thought of as a three-dimensional array of leaves reflecting the emitted bat call. The received echo is therefore a superposition of many reflections. In this work we suggest that the classification of these echoes might not be such a troublesome routine for bats as formerly thought. We present a rather simple approach to classifying signals from a large database of plant echoes that were created by ensonifying plants with a frequency-modulated bat-like ultrasonic pulse. Our algorithm uses the spectrogram of a single echo from which it only uses features that are undoubtedly accessible to bats. We used a standard machine learning algorithm (SVM) to automatically extract suitable linear combinations of time and frequency cues from the spectrograms such that classification with high accuracy is enabled. This demonstrates that ultrasonic echoes are highly informative about the species membership of an ensonified plant, and that this information can be extracted with rather simple, biologically plausible analysis. Thus, our findings provide a new explanatory basis for the poorly understood observed abilities of bats in classifying vegetation and other complex objects