9,740 research outputs found

    Refined localization of human peroxisomal 3-oxoacyl-CoA thiolase (ACAA)to 3p22

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    The chromosomal localisation of the human gene coding for peroxisomal 3-oxoacyl-CoA thiolase (ACAA) was determined by human-hamster somatic cell hybrids and fluorescence in situ hybridisation, using cDNA and genomic probes, respectively. The results allowed an exclusion of the previously suggested presence of a second site for ACAA on chromosome 11 and an assignment of the gene to a single chromosome band (3p22)

    Assignment of the human connexin 32 gene (GJB1) to band Xq13.

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    The chromosomal localization of the human gene coding for connexin 32 (GJB1) was determined by in situ suppression hybridization (ISSH). The results allowed assignment of the gene to band Xq13, thus refining previous localization data obtained by means of somatic cell hybrid analysis

    Metadata Representations for Queryable ML Model Zoos

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    Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc

    A Manifesto of Nodalism

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    This paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music

    Optimizing ML Inference Queries Under Constraints

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    The proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
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