Roaring lions and chirruping lemurs: How the brain encodes sound objects in space.

The dual-stream model of auditory processing postulates separate processing streams for sound meaning and for sound location. The present review draws on evidence from human behavioral and activation studies as well as from lesion studies to argue for a position-linked representation of sound objects that is distinct both from the position-independent representation within the ventral/What stream and from the explicit sound localization processing within the dorsal/Where stream.

Massive protostars as gamma-ray sources.

Massive protostars have associated bipolar outflows with velocities of hundreds of km s-1. Such outflows can produce strong shocks when they interact with the ambient medium leading to regions of nonthermal radio emission. Aims. We aim at exploring under which conditions relativistic particles are accelerated at the terminal shocks of the protostellar jets and whether they can produce significant gamma-ray emission. Methods. We estimate the conditions necessary for particle acceleration up to very high energies and gamma-ray production in the nonthermal hot spots of jets associated with massive protostars embedded in dense molecular clouds. Results. We show that relativistic bremsstrahlung and proton-proton collisions can make molecular clouds with massive young stellar objects detectable by the Fermi satellite at MeV-GeV energies and by Cherenkov telescope arrays in the GeV-TeV range. Conclusions. Gamma-ray astronomy can be used to probe the physical conditions in star-forming regions and particle acceleration processes in the complex environment of massive molecular clouds.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Shout and Act: an Algorithm for Digital Objects Preservation Inspired from Rescue Robots

We adapt the Shout and Act algorithm to Digital Objects Preservation where agents explore file systems looking for digital objects to be preserved (victims). When they find something they “shout” so that agent mates can hear it. The louder the shout, the urgent or most important the finding is. Louder shouts can also refer to closeness. We perform several experiments to show that this system works very scalably, showing that heterogeneous teams of agents outperform homogeneous ones over a wide range of tasks complexity. The target at-risk documents are MS Office documents (including an RTF file) with Excel content or in Excel format. Thus, an interesting conclusion from the experiments is that fewer heterogeneous (varying skills) agents can equal the performance of many homogeneous (combined super-skilled) agents, implying significant performance increases with lower overall cost growth. Our results impact the design of Digital Objects Preservation teams: a properly designed combination of heterogeneous teams is cheaper and more scalable when confronted with uncertain maps of digital objects that need to be preserved. A cost pyramid is proposed for engineers to use for modeling the most effective agent combinations

A new Ophiodothella species associated with leaf spots on Annona squamosa in Brazil

A new species of Ophiodothella, O. annonae (Phyllachoraceae, Ascomycetes) is described and illustrated. The fungus causes brown lesions on leaves of Annonae squamosa, which can fall off prematurely, and is characterized by fusiform, slightly curved, biguttulate ascospores with a conspicuous central concentration of cytoplasm. This is the first species of Ophiodotella found on leaves of Annonaceae in Brazil.

Quasi-objects and actors in the web of belief. Formation of a national nuclear waste management system: the case of Finland

Paper presented in ISA RC23 meeting, Gothenburg July 16th 2010

Consistency from the perspective of an experimental systems approach to the sciences and their epistemic objects

It is generally accepted that the development of the modern sciences is rooted in experiment. Yet for a long time, experimentation did not occupy a prominent role, neither in philosophy nor in history of science. With the 'practical turn' in studying the sciences and their history, this has begun to change. This paper is concerned with systems and cultures of experimentation and the consistencies that are generated within such systems and cultures. The first part of the paper exposes the forms of historical and structural coherence that characterize the experimental exploration of epistemic objects. In the second part, a particular experimental culture in the life sciences is briefly described as an example. A survey will be given of what it means and what it takes to analyze biological functions in the test tube.

Circular Spectropolarimetric Studies of DQ White Dwarfs

This thesis summarizes studies of a class of white dwarfs (WDs) called DQ WDs. White dwarfs are the remnants of ordinary stars like our Sun that have run out of nuclear fuel. WDs are classified according to the composition of their atmosphere and DQ WDs have an atmosphere made of helium and carbon. The carbon comes in either atomic or molecular form and in some cases the strong spectral absorption features cover the entire optical wavelength region. The research presented here utilizes spectropolarimetry, which is an observational technique that combines spectroscopy and polarization. Separately these allow to study the composition of a target and the inhomogeneous distribution of matter in the target. Put together they form a powerful tool to probe the physical properties in the atmosphere of a star. It is espacially good for detecting magnetic fields. The papers in this thesis describe efforts to do a survey of DQ white dwarfs with spectropolarimetry in order to search for magnetic fields in them. Paper I describes the discovery of a new magnetic cool DQ white dwarf, GJ841B. Initial modeling of molecular features on DQ WDs showed inconsistencies with observations. The first possible solution to this problem was stellar spots on these WDs. To investigate the matter, two DQ WDs were monitored for photometric variability that could arise from the presence of such spots. Paper II summarizes this short campaign and reports the negative results. Paper III reports observations of the rest of the objects in our survey. The paper includes the discovery of polarization from another cool DQ white dwarf, bringing the total of known magnetic cool DQs to three. Unfortunately the model used in this thesis cannot, in its present state, be used to model these objects nor are the observations of high enough spectroscopic resolution to do so.

Probabilistic position and orientation estimation of rigid objects using an RGB-D sensor

The recent emergence of low-cost RGB-D sensors has brought new opportunities for robotics by providing affordable devices that can provide synchronized images with both color and depth information. In this thesis, recent work on pose estimation utilizing RGBD sensors is reviewed. Also, a pose recognition system for rigid objects using RGB-D data is implemented. The implementation uses half-edge primitives extracted from the RGB-D images for pose estimation. The system is based on the probabilistic object representation framework by Detry et al., which utilizes Nonparametric Belief Propagation for pose inference. Experiments are performed on household objects to evaluate the performance and robustness of the system.

Segmentation of overlapping convex objects

This thesis presents a framework for segmentation of clustered overlapping convex objects. The proposed approach is based on a three-step framework in which the tasks of seed point extraction, contour evidence extraction, and contour estimation are addressed. The state-of-art techniques for each step were studied and evaluated using synthetic and real microscopic image data. According to obtained evaluation results, a method combining the best performers in each step was presented. In the proposed method, Fast Radial Symmetry transform, edge-to-marker association algorithm and ellipse fitting are employed for seed point extraction, contour evidence extraction and contour estimation respectively. Using synthetic and real image data, the proposed method was evaluated and compared with two competing methods and the results showed a promising improvement over the competing methods, with high segmentation and size distribution estimation accuracy.