Two is better than one: A diploid genotype for neural networks

In nature the genotype of many organisms exhibits diploidy, i.e., it includes two copies of every gene. In this paper we describe the results of simulations comparing the behavior of haploid and diploid populations of ecological neural networks living in both fixed and changing environments. We show that diploid genotypes create more variability in fitness in the population than haploid genotypes and buffer better environmental change; as a consequence, if one wants to obtain good results for both average and peak fitness in a single population one should choose a diploid population with an appropriate mutation rate. Some results of our simulations parallel biological findings.     

RUSHES—an annotation and retrieval engine for multimedia semantic units

Multimedia analysis and reuse of raw un-edited audio visual content known as rushes is gaining acceptance by a large number of research labs and companies. A set of research projects are considering multimedia indexing, annotation, search and retrieval in the context of European funded research, but only the FP6 project RUSHES is focusing on automatic semantic annotation, indexing and retrieval of raw and un-edited audio-visual content. Even professional content creators and providers as well as home-users are dealing with this type of content and therefore novel technologies for semantic search and retrieval are required. In this paper, we present a summary of the most relevant achievements of the RUSHES project, focusing on specific approaches for automatic annotation as well as the main features of the final RUSHES search engine.     

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like ‘rooms’ and ‘corridors’) with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for example, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.     

Making random permutations from physically unclonable constants

The recent availability of reliable schemes for physically unclonable constants (PUC) opens interesting possibilities in the field of security. In this paper, we explore the possibility of using PUCs to embed in a chip random permutations to be used, for example, as building blocks in cryptographic constructions such as sponge functions, substitution–permutation networks, and so on. We show that the most difficult part is the generation of random integers using as the only randomness source the bit-string produced by the PUC. In order to solve the integer generation problem, we propose a partial rejection method that allows the designer to trade-off between entropy and efficiency. The results show that the proposed schemes can be implemented with reasonable complexity.     

Dealing with logical omniscience: Expressiveness and pragmatics

We examine four approaches for dealing with the logical omniscience problem and their potential applicability: the syntactic approach, awareness, algorithmic knowledge, and impossible possible worlds. Although in some settings these approaches are equi-expressive and can capture all epistemic states, in other settings of interest (especially with probability in the picture), we show that they are not equi-expressive. We then consider the pragmatics of dealing with logical omniscience—how to choose an approach and construct an appropriate model.     

An adaptive learning regulator for uncertain minimum phase systems with undermodeled unknown exosystems

The design of an adaptive learning regulator is addressed for uncertain minimum phase linear systems (with known bounds, known upper bound on system order, known relative degree, known high frequency gain sign) and for unknown exosystems (with unknown order, uncertain frequencies). On the basis of a known bound on system uncertainties and a known bound on the modeled exosystem frequencies, a new adaptive output error feedback control algorithm is proposed which guarantees exponential convergence of both the output and the control input errors into residual bounds which decrease as the exosystem modeling error decreases. Exponential convergence of both errors to zero is obtained when the regulator exactly models all exosystem excited frequencies, while asymptotic convergence of both errors to zero is achieved when the actual exosystem is overmodeled by the regulator. The new algorithm generalizes existing learning controllers since, in the case of periodic references and/or disturbances, the knowledge of the period is not required.     

On‐Chip Magnetic Platform for Single‐Particle Manipulation with Integrated Electrical Feedback

Methods for the manipulation of single magnetic particles have become very interesting, in particular for in vitro biological studies. Most of these studies require an external microscope to provide the operator with feedback for controlling the particle motion, thus preventing the use of magnetic particles in high‐throughput experiments. In this paper, a simple and compact system with integrated electrical feedback is presented, implementing in the very same device both the manipulation and detection of the transit of single particles. The proposed platform is based on zig‐zag shaped magnetic nanostructures, where transverse magnetic domain walls are pinned at the corners and attract magnetic particles in suspension. By applying suitable external magnetic fields, the domain walls move to the nearest corner, thus causing the step by step displacement of the particles along the nanostructure. The very same structure is also employed for detecting the bead transit. Indeed, the presence of the magnetic particle in suspension over the domain wall affects the depinning field required for its displacement. This characteristic field can be monitored through anisotropic magnetoresistance measurements, thus implementing an integrated electrical feedback of the bead transit. In particular, the individual manipulation and detection of single 1‐μm sized beads is demonstrated.