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Barra's Risk Factor Analysis uses a multi-factor model that incorporates more than 40 different data elements for each stock and evaluates their contribution to risk.
In addition, due to multidisciplinary nature of smart city application domains, engagement with domain experts is needed to identify basic relationships and dependencies between different data elements.
A better representation of the hardware architecture can be done through three different data elements: the CPU architecture (e.g. x86, PowerPC), the GPU or accelerator architecture (e.g. Nvidia GeForce GTX 780, AMD Radeon HD 7970, Intel PHI), and possibly a machine or supercomputer architecture identification (e.g. Cray XK7, IBM Blue Gene/Q, commodity Infiniband cluster, etc).
The data used in this study come from multiple sources with different data elements and quality.
We designed several models that describe the different data elements, describe their relationships and hold the data.
They may capture data using different instruments, different databases, different data architectures, different data elements and immensely different formats.
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For the METABRIC study we developed 79 CDEs that correspond to 33 different data element concepts.
The curator tackles the complex problem of describing the relationship among each different data element (gene, allele, genetic population, phenotype and environment) and linking each element to all other entries in the database using a combination of bioinformatic tools, ontologies and free text.
On this point, the UEPWZ takes the advantage of allocating different protection level on different protected video data elements depending on their overall impact on the decoded video sequence.
CAUEP improves UEPWZ by adapting the protection levels of different video data element, to the content of each frame.
In this field, the most widespread techniques are based on unequal FEC, that is, different portions of compressed data elements (e.g., H.264/AVC stream) are not allocated the same amount of redundancy bits.
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