RDF/S)XML Linguistic Annotation of Semantic Web Pages

Although with the Semantic Web initiative much research on web pages semantic annotation has already done by AI researchers, linguistic text annotation, including the semantic one, was originally developed in Corpus Linguistics and its results have been somehow neglected by AI. ..

RDF/S)XML Linguistic Annotation of Semantic Web Pages

Although with the Semantic Web initiative much research on web pages semantic annotation has already done by AI researchers, linguistic text annotation, including the semantic one, was originally developed in Corpus Linguistics and its results have been somehow neglected by AI. ..

On the influence of spatial information for hyper-spectral satellite imaging characterization

Land-use classification for hyper-spectral satellite images requires a previous step of pixel characterization. In the easiest case, each pixel is characterized by its spectral curve. The improvementof the spectral and spatial resolution in hyper-spectral sensors has led to very large data sets. Some researches have focused on better classifiers that can handle big amounts of data. Others have faced the problem of band selection to reduce the dimensionality of the feature space. However, thanks to the improvement in the spatial resolution of the sensors, spatial information may also provide new featuresfor hyper-spectral satellite data. Here, an study on the influence of spectral-spatial features combined with an unsupervised band selection method is presented. The results show that it is possible to reduce very significantly the number of spectral bands required while having an adequate description of the spectral-spatial characteristics of the image for pixel classification tasksThis work has been partly supported by grant FPI PREDOC/2007/20 from Fundació Caixa Castelló-Bancaixa and projects CSD2007-00018 (Consolider Ingenio 2010) and AYA2008-05965-C04-04 from the Spanish Ministry of Science and Innovatio

Auto-Classifier: A Robust Defect Detector Based on an AutoML Head

The dominant approach for surface defect detection is the use of hand-crafted feature-based methods. However, this falls short when conditions vary that affect extracted images. So, in this paper, we sought to determine how well several state-of-the-art Convolutional Neural Networks perform in the task of surface defect detection. Moreover, we propose two methods: CNN-Fusion, that fuses the prediction of all the networks into a final one, and Auto-Classifier, which is a novel proposal that improves a Convolutional Neural Network by modifying its classification component using AutoML. We carried out experiments to evaluate the proposed methods in the task of surface defect detection using different datasets from DAGM2007. We show that the use of Convolutional Neural Networks achieves better results than traditional methods, and also, that Auto-Classifier out-performs all other methods, by achieving 100% accuracy and 100% AUC results throughout all the datasets.Comment: 12 pages, 2 figures. Published in ICONIP2020, proceedings published in the Springer's series of Lecture Notes in Computer Scienc

Effect of External Noise Correlation in Optical Coherence Resonance

Coherence resonance occurring in semiconductor lasers with optical feedback is studied via the Lang-Kobayashi model with external non-white noise in the pumping current. The temporal correlation and the amplitude of the noise have a highly relevant influence in the system, leading to an optimal coherent response for suitable values of both the noise amplitude and correlation time. This phenomenon is quantitatively characterized by means of several statistical measures.Comment: RevTeX, 4 pages, 7 figure

Explicit Evidence Systems with Common Knowledge

Justification logics are epistemic logics that explicitly include justifications for the agents' knowledge. We develop a multi-agent justification logic with evidence terms for individual agents as well as for common knowledge. We define a Kripke-style semantics that is similar to Fitting's semantics for the Logic of Proofs LP. We show the soundness, completeness, and finite model property of our multi-agent justification logic with respect to this Kripke-style semantics. We demonstrate that our logic is a conservative extension of Yavorskaya's minimal bimodal explicit evidence logic, which is a two-agent version of LP. We discuss the relationship of our logic to the multi-agent modal logic S4 with common knowledge. Finally, we give a brief analysis of the coordinated attack problem in the newly developed language of our logic

proGenomes2: an improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes

Microbiology depends on the availability of annotated microbial genomes for many applications. Comparative genomics approaches have been a major advance, but consistent and accurate annotations of genomes can be hard to obtain. In addition, newer concepts such as the pan-genome concept are still being implemented to help answer biological questions. Hence, we present proGenomes2, which provides 87 920 high-quality genomes in a user-friendly and interactive manner. Genome sequences and annotations can be retrieved individually or by taxonomic clade. Every genome in the database has been assigned to a species cluster and most genomes could be accurately assigned to one or multiple habitats. In addition, general functional annotations and specific annotations of antibiotic resistance genes and single nucleotide variants are provided. In short, proGenomes2 provides threefold more genomes, enhanced habitat annotations, updated taxonomic and functional annotation and improved linkage to the NCBI BioSample database. The database is available at http://progenomes.embl.de/

Characterization of Turing diffusion-driven instability on evolving domains

In this paper we establish a general theoretical framework for Turing diffusion-driven instability for reaction-diffusion systems on time-dependent evolving domains. The main result is that Turing diffusion-driven instability for reaction-diffusion systems on evolving domains is characterised by Lyapunov exponents of the evolution family associated with the linearised system (obtained by linearising the original system along a spatially independent solution). This framework allows for the inclusion of the analysis of the long-time behavior of the solutions of reaction-diffusion systems. Applications to two special types of evolving domains are considered: (i) time-dependent domains which evolve to a final limiting fixed domain and (ii) time-dependent domains which are eventually time periodic. Reaction-diffusion systems have been widely proposed as plausible mechanisms for pattern formation in morphogenesis