50 research outputs found
Greedy Maximization Framework for Graph-based Influence Functions
The study of graph-based submodular maximization problems was initiated in a
seminal work of Kempe, Kleinberg, and Tardos (2003): An {\em influence}
function of subsets of nodes is defined by the graph structure and the aim is
to find subsets of seed nodes with (approximately) optimal tradeoff of size and
influence. Applications include viral marketing, monitoring, and active
learning of node labels. This powerful formulation was studied for
(generalized) {\em coverage} functions, where the influence of a seed set on a
node is the maximum utility of a seed item to the node, and for pairwise {\em
utility} based on reachability, distances, or reverse ranks.
We define a rich class of influence functions which unifies and extends
previous work beyond coverage functions and specific utility functions. We
present a meta-algorithm for approximate greedy maximization with strong
approximation quality guarantees and worst-case near-linear computation for all
functions in our class. Our meta-algorithm generalizes a recent design by Cohen
et al (2014) that was specific for distance-based coverage functions.Comment: 8 pages, 1 figur
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology
Abstract Background In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN). Results The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters. Conclusions This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.http://deepblue.lib.umich.edu/bitstream/2027.42/78257/1/1471-2105-11-505.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/2/1471-2105-11-505-S1.DOChttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/3/1471-2105-11-505.pdfPeer Reviewe
Reverse nearest neighbor queries in fixed dimension
Reverse nearest neighbor queries are defined as follows: Given an input
point-set P, and a query point q, find all the points p in P whose nearest
point in P U {q} \ {p} is q. We give a data structure to answer reverse nearest
neighbor queries in fixed-dimensional Euclidean space. Our data structure uses
O(n) space, its preprocessing time is O(n log n), and its query time is O(log
n).Comment: 7 pages, 3 figures; typos corrected; more background material on
compressed quadtree