Exploration and synthesis of dynamic data sets in telecom network applications

Author(s):  
Ch. Ykman-Couvreur ◽  
J. Lambrecht ◽  
D. Verkest ◽  
F. Catthoor ◽  
H. De Man
2019 ◽  
Vol 30 (2) ◽  
pp. 1-26
Author(s):  
Lei Li ◽  
Yuqi Chu ◽  
Guanfeng Liu ◽  
Xindong Wu

Along with the fast development of network applications, network research has attracted more and more attention, where one of the most important research directions is networked multi-label classification. Based on it, unknown labels of nodes can be inferred by known labels of nodes in the neighborhood. As both the scale and complexity of networks are increasing, the problems of previously neglected system overhead are turning more and more seriously. In this article, a novel multi-objective optimization-based networked multi-label seed node selection algorithm (named as MOSS) is proposed to improve both the prediction accuracy for unknown labels of nodes from labels of seed nodes during classification and the system overhead for mining the labels of seed nodes with third parties before classification. Compared with other algorithms on several real networked data sets, MOSS algorithm not only greatly reduces the system overhead before classification but also improves the prediction accuracy during classification.


2002 ◽  
Vol 1 (1) ◽  
pp. 106-140 ◽  
Author(s):  
Ch. Ykman-Couvreur ◽  
J. Lambrecht ◽  
A. Van Der Togt ◽  
F. Catthoor ◽  
H. De Man

2017 ◽  
Author(s):  
Harun Mustafa ◽  
André Kahles ◽  
Mikhail Karasikov ◽  
Gunnar Rätsch

AbstractMuch of the DNA and RNA sequencing data available is in the form of high-throughput sequencing (HTS) reads and is currently unindexed by established sequence search databases. Recent succinct data structures for indexing both reference sequences and HTS data, along with associated metadata, have been based on either hashing or graph models, but many of these structures are static in nature, and thus, not well-suited as backends for dynamic databases.We propose a parallel construction method for and novel application of the wavelet trie as a dynamic data structure for compressing and indexing graph metadata. By developing an algorithm for merging wavelet tries, we are able to construct large tries in parallel by merging smaller tries constructed concurrently from batches of data.When compared against general compression algorithms and those developed specifically for graph colors (VARI and Rainbowfish), our method achieves compression ratios superior to gzip and VARI, converging to compression ratios of 6.5% to 2% on data sets constructed from over 600 virus genomes.While marginally worse than compression by bzip2 or Rainbowfish, this structure allows for both fast extension and query. We also found that additionally encoding graph topology metadata improved compression ratios, particularly on data sets consisting of several mutually-exclusive reference genomes.It was also observed that the compression ratio of wavelet tries grew sublinearly with the density of the annotation matrices.This work is a significant step towards implementing a dynamic data structure for indexing large annotated sequence data sets that supports fast query and update operations. At the time of writing, no established standard tool has filled this niche.


2019 ◽  
Vol 18 (3) ◽  
pp. 305-326
Author(s):  
Vanessa Chang

Created with digital motion capture, or mocap, the virtual dances Ghostcatching and as.phyx.ia render movement abstracted from choreographic bodies. These depictions of gestural doubles or ‘ghosts’ trigger a sense of the uncanny rooted in mocap’s digital processes. Examining these material processes, this article argues that this digital optical uncanny precipitates from the intersubjective relationship of performer, technology, and spectator. Mocap interpolates living bodies into a technologized visual field that parses these bodies as dynamic data sets, a process by which performing bodies and digital capture technologies coalesce into the film’s virtual body. This virtual body signals a computational agency at its heart, one that choreographs the intersubjective embodiments of real and virtual dancers, and spectators. Destabilizing the human body as a locus of perception, movement, and sensation, mocap triggers uncanny uncertainty in human volition. In this way, Ghostcatching and as.phyx.ia reflect the infiltration of computer vision technologies, such as facial recognition, into numerous aspects of contemporary life. Through these works, the author hopes to show how the digital gaze of these algorithms, imperceptible to the human eye, threatens individual autonomy with automation.


2010 ◽  
Vol 50 (3) ◽  
pp. 533-562 ◽  
Author(s):  
Roberto Dantas de Pinho ◽  
Maria Cristina Ferreira de Oliveira ◽  
Alneu de Andrade Lopes
Keyword(s):  

Axioms ◽  
2016 ◽  
Vol 5 (4) ◽  
pp. 26 ◽  
Author(s):  
Melanie Weber ◽  
Jürgen Jost ◽  
Emil Saucan

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