47,215 research outputs found

    Electronic structure and the glass transition in pnictide and chalcogenide semiconductor alloys. Part II: The intrinsic electronic midgap states

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    We propose a structural model that treats in a unified fashion both the atomic motions and electronic excitations in quenched melts of pnictide and chalcogenide semiconductors. In Part I (submitted to J. Chem. Phys.), we argued these quenched melts represent aperiodic ppσpp\sigma-networks that are highly stable and, at the same time, structurally degenerate. These networks are characterized by a continuous range of coordination. Here we present a systematic way to classify these types of coordination in terms of discrete coordination defects in a parent structure defined on a simple cubic lattice. We identify the lowest energy coordination defects with the intrinsic midgap electronic states in semiconductor glasses, which were argued earlier to cause many of the unique optoelectronic anomalies in these materials. In addition, these coordination defects are mobile and correspond to the transition state configurations during the activated transport above the glass transition. The presence of the coordination defects may account for the puzzling discrepancy between the kinetic and thermodynamic fragility in chalcogenides. Finally, the proposed model recovers as limiting cases several popular types of bonding patterns proposed earlier, including: valence-alternation pairs, hypervalent configurations, and homopolar bonds in heteropolar compounds.Comment: 17 pages, 15 figures, revised version, final version to appear in J. Chem. Phy

    Linking Visual Development and Learning to Information Processing: Preattentive and Attentive Brain Dynamics

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    National Science Foundation (SBE-0354378); Office of Naval Research (N00014-95-1-0657

    CentralNet: a Multilayer Approach for Multimodal Fusion

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    This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past multimodal approaches either work by projecting the features of different modalities into the same space, or by coordinating the representations of each modality through the use of constraints, our approach borrows from both visions. More specifically, assuming each modality can be processed by a separated deep convolutional network, allowing to take decisions independently from each modality, we introduce a central network linking the modality specific networks. This central network not only provides a common feature embedding but also regularizes the modality specific networks through the use of multi-task learning. The proposed approach is validated on 4 different computer vision tasks on which it consistently improves the accuracy of existing multimodal fusion approaches

    SNOMED CT standard ontology based on the ontology for general medical science

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    Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks ofdefinitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-levelSCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. Theapproach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundryontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-levelontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555annotations. It is publicly available through the bioportal athttp://bioportal.bioontology.org/ontologies/SCTO/. Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semanticinteroperability among distributed electronic health records. In addition, the populated ontology can be used forthe automation of mobile health applications
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