2.2. Formulation Our basic idea is trying to exploit the intrinsic relations in the matrix data: both the neighbor structures in each view and the semantic correlations between views. Corre- spondingly, two types of losses will be employed to guide the hash function learning in our formulation...
Computer Science - Data Structures and AlgorithmsA method for identifying graphs using MD5 hashing is presented. This allows fast graph equality comparisons and can also be used to facilitate graph isomorphism testing. The graphs can be labeled or unlabeled. The method identifies vertices by hashing ...
Techniques for implementing resilient hashing with compression are provided. In some embodiments, a network device can maintain a compressed partition and an uncompressed partition
table Offset table Φ =1381 pixels in 1282 image =382 (=1444) =182 Sparse 3D data Hash table Offset table Φ =41,127 voxels in 1283 volume =353 (=42,875) =193 Figure 1: Representation of sparse spatial data using nearly minimal perfect hashes, illustrated on coarse 2D and 3D ...
1. Introduction Nearest Neighbors (NN) search is a fundamental prob- lem and has found applications in many computer vision tasks [23, 10, 29]. A number of efficient algorithms, based on pre-built index structures (e.g. KD-tree [4] and R- tree [2]), have been proposed for nearest...
Authenticated encryption satisfies the basic need for authenticity and confidentiality in our information infrastructure. In this paper, we provide the spe
Most network load balancers available today are implemented in software that runs on general purpose computer systems, such as Intel x86-based systems. This is largely because the algorithms and data structures used by these network load balancers require an amount of memory that exceeds the memory...
Hash maps: Also known by the name hash tables, hash maps indicate data structures used to store keys/value pairs vide an associative array abstract data type. • Bloom filters: Probabilistic structures of data that are used to ascertain a specific data element queried in a large set inside ...
Information Science Cornell University, Ithaca, NY 14853 Arnd Christian König Microsoft Research Microsoft Corporation Redmond, WA 98052 ABSTRACT Efficient (approximate) computation of set similarity in very large datasets is a common task with many applications in information retrieval and data ...
Next, we show how to drive lock-free operations based on the data structures. 280 W. Li et al. 3.2 Basic Operations In this section, we consider three basic operations, i.e., Get, Put, and Delete. We focus on the use cases without the kicking and rehash processes and leave more ...