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Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.
Another key difference is the addressing of values. JSON has objects with a simple "key" to "value" mapping, whereas in XML addressing happens on "nodes", which all receive a unique ID via the XML processor. Additionally, the XML standard defines a common attribute xml:id, that can be used by the user, to set an ID explicitly.
Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text , where each line of the file typically represents one data record .
In Microsoft SQL Server, the leaf node of the clustered index corresponds to the actual data, not simply a pointer to data that resides elsewhere, as is the case with a non-clustered index. [5] Each relation can have a single clustered index and many unclustered indices.
SQL—Structured Query Language; SRAM—Static Random-Access Memory; SSA—Static Single Assignment; SSD—Software Specification Document; SSD—Solid-State Drive; SSDP—Simple Service Discovery Protocol; SSE—Streaming SIMD Extensions; SSH—Secure Shell; SSI—Server Side Includes; SSI—Single-System Image; SSI—Small-Scale Integration
SQL: New aggregate functions MEDIAN, STDDEV, VAR, CORR, and CORR_SPEARMAN; SQL: New geometry conversion function as_geo_json; SQL conditions revised; SQL: New window function NTILE; SQL: SELECT, INTO target – host variables can now be declared inline even when the FROM-clause is dynamic; SQL: New type conversion functions to_clob and to_blob