A data structure is a particular way storing and organizing data in a computer for efficient access and modification. Data structures are designed for a specific purpose. Examples include arrays, linked lists, and classes. Here are 3,261 public repositories matching this topic... Language: C ...
Agrona provides a library of data structures and utility methods that are a common need when building high-performance applications in Java. Many of these utilities are used in the Aeron efficient reliable UDP unicast, multicast, and IPC message transport and provides high-performance buffer ...
Powerful, flexiblegroup byfunctionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data Make iteasy to convertragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects ...
2 Methods performance on various biotechnologies. On the heatmap, the rows represent the biotechnologies, the columns represent the methods, and each value in the figure represents the NMI values. Extended Data Fig. 3 User guidance. Recommend the suitable methods for users according to the data ...
Representation of molecular structures with persistent homology for machine learning applications in chemistry Article Open access 26 June 2020 The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules Article Open access 01 May 2020 Introduction...
Downstream analyses are designed to annotate MAGs, and they include gene prediction, gene functional annotation, taxonomic classification and profiling. In Section 4, we introduce the computational requisition, comparative performance and selective guidance of these tools. We also discuss the potential ...
However, to the best of our knowledge, today there is no implementation spanning the complete universe of coverage structures, and even less so supporting combining all different coverage types. Typically, coverage-oriented tools concentrate on raster data while point clouds and meshes are dealt with...
of privacy preservation are described, and their impact on machine learning performance is compared. As a baseline random splits were considered, despite the fact that these are not trivial to execute in a federated setting, under the constraint of consistent mapping of identical structures. ...
$ go test -v -bench '.*' \ > github.com/timtadh/data-structures/hashtable > github.com/timtadh/data-structures/tree/... > github.com/timtadh/data-structures/trie BenchmarkGoMap 50000 30051 ns/op BenchmarkMLHash 20000 78840 ns/op BenchmarkHash 20000 81012 ns/op BenchmarkTST 10000...
Efficiency (CPU time taken to encode or decode, and the size of the encoded structure) is also often an afterthought. For example, Java’s built-in serialization is notorious for its bad performance and bloated encoding [8]. For these reasons it’s generally a bad idea to use your languag...