Ontologies and Knowledge Aggregation in the Active Semantic Learning System

5 PagesInternational audienceThe construction of semantic-based learning systems depends on the development of ontologies and the capacity to integrate and exploit knowledge using semantic technologies, notably RDF and ontologies. In this paper we present some concepts and ontologies defined in the context of the Active Semantic Learning System (Active SLS) that are used to describe resources and the semantic relation between these concepts defined in different ontologies. The purpose is to obtain a learning system that is capable of aggregating knowledge from different sources from the web and to exploiting that knowledge for the benefit of the learner

On Stability in Control Systems

Stability definitions for generalized control systems - dynamical system

Guided Depth Upsampling for Precise Mapping of Urban Environments

We present an improved model for MRF-based depth upsampling, guided by image- as well as 3D surface normal features. By exploiting the underlying camera model we define a novel regularization term that implicitly evaluates the planarity of arbitrary oriented surfaces. Our method improves upsampling quality in scenes composed of predominantly planar surfaces, such as urban areas. We use a synthetic dataset to demonstrate that our approach outperforms recent methods that implement distance-based regularization terms. Finally, we validate our approach for mapping applications on our experimental vehicle.Comment: 6 pages, 6 figure

On the structure of cortical microcircuits inferred from small sample sizes

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering. SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering.Postprint (author's final draft

A Federated Approach for Interoperating AEC/FM Ontologies

International audienceOver the last few years, the benefits of applying ontologies (semantic graph modelling) for Architecture, Engineering, Construction and Facility Management (AEC/FM) industry have been recognized by several researchers and industry stakeholders. One of the main motivations is because it eases AEC data manipulation and representation. However, a research question that still remains open is how to take advantage of semantic web technologies to interoperate the AEC/FM and other ontologies in a flexible and dynamical way in order to solve data structure heterogeneity problem. Because of this, we propose in this paper to apply a rule-based federated architecture to answer this research question

A Rule Based System for Semantical Enrichment of Building Information Exchange

International audienceIn the area of building construction and management, the dematerial-ization of data and processes has been a global issue for the past 10 years. Go-ing beyond the geometric representation of a building, Building Information Modeling (BIM) is an approach that aims at integrating into one single system heterogeneous data and processes from different actors. Such integration is a complex and fastidious task. The implementation of the related processes for data querying, retrieval or modification is not less difficult. To tackle this prob-lem, we have developed a novel approach based on Semantic Web technolo-gies. In doing so, we have defined an ontology inspired on IFC standard for rep-resenting building information. On top of this ontology, we have defined and implemented a set of SWRL rules. The paper at hand describes these rules and their application in the context of building information handling (notably by means of IFC files

Many Attractors, Long Chaotic Transients, and Failure in Small-World Networks of Excitable Neurons

We study the dynamical states that emerge in a small-world network of recurrently coupled excitable neurons through both numerical and analytical methods. These dynamics depend in large part on the fraction of long-range connections or `short-cuts' and the delay in the neuronal interactions. Persistent activity arises for a small fraction of `short-cuts', while a transition to failure occurs at a critical value of the `short-cut' density. The persistent activity consists of multi-stable periodic attractors, the number of which is at least on the order of the number of neurons in the network. For long enough delays, network activity at high `short-cut' densities is shown to exhibit exceedingly long chaotic transients whose failure-times averaged over many network configurations follow a stretched exponential. We show how this functional form arises in the ensemble-averaged activity if each network realization has a characteristic failure-time which is exponentially distributed.Comment: 14 pages 23 figure

Stability in general control systems <stabilitat in allgemeinen regelungssystemen<

Stability in ordinary control system

Mean-field equations for stochastic firing-rate neural fields with delays: Derivation and noise-induced transitions

In this manuscript we analyze the collective behavior of mean-field limits of large-scale, spatially extended stochastic neuronal networks with delays. Rigorously, the asymptotic regime of such systems is characterized by a very intricate stochastic delayed integro-differential McKean-Vlasov equation that remain impenetrable, leaving the stochastic collective dynamics of such networks poorly understood. In order to study these macroscopic dynamics, we analyze networks of firing-rate neurons, i.e. with linear intrinsic dynamics and sigmoidal interactions. In that case, we prove that the solution of the mean-field equation is Gaussian, hence characterized by its two first moments, and that these two quantities satisfy a set of coupled delayed integro-differential equations. These equations are similar to usual neural field equations, and incorporate noise levels as a parameter, allowing analysis of noise-induced transitions. We identify through bifurcation analysis several qualitative transitions due to noise in the mean-field limit. In particular, stabilization of spatially homogeneous solutions, synchronized oscillations, bumps, chaotic dynamics, wave or bump splitting are exhibited and arise from static or dynamic Turing-Hopf bifurcations. These surprising phenomena allow further exploring the role of noise in the nervous system.Comment: Updated to the latest version published, and clarified the dependence in space of Brownian motion