A Self-Stabilizing Algorithm for a Maximal 2-Packing in a Cactus Graph Under Any Scheduler

In this paper, we present a self-stabilizing algorithm that computes a maximal 2-packing set in a cactus under the adversarial scheduler. The cactus is a network topology such that any edge belongs to at most one cycle. The cactus has important applications in telecommunication networks, location problems, and biotechnology, among others. We assume that the value of each vertex identifier can take any value of length [Formula: see text] bits. The execution time of this algorithm is [Formula: see text] rounds or [Formula: see text] time steps. Our algorithm matches the state of the art results for this problem, following an entirely different approach. Our approach allows the computation of the maximum 2-packing when the cactus is a ring.

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OPEN PROBLEMS IN COOPERATIVE LOCATION GAMES

International Game Theory Review ◽

10.1142/s021919891340015x ◽

2013 ◽

Vol 15 (03) ◽

pp. 1340015 ◽

Cited By ~ 3

Author(s):

VITO FRAGNELLI ◽

STEFANO GAGLIARDO

Keyword(s):

Game Theory ◽

Objective Function ◽

State Of The Art ◽

The State ◽

Location Problems ◽

Open Problems ◽

Location Games

Location problems describe those situations in which one or more facilities have to be placed in a region trying to optimize a suitable objective function. Game theory has been used as a tool to solve location problems and this paper is devoted to describe the state-of-the-art of the research on location problems through the tools of game theory. Particular attention is given to the problems that are still open in the field of cooperative location game theory.

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Network hub location problems: The state of the art

European Journal of Operational Research ◽

10.1016/j.ejor.2007.06.008 ◽

2008 ◽

Vol 190 (1) ◽

pp. 1-21 ◽

Cited By ~ 484

Author(s):

Sibel Alumur ◽

Bahar Y. Kara

Keyword(s):

State Of The Art ◽

The State ◽

Location Problems ◽

Hub Location

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Unifying Node Labels, Features, and Distances for Deep Network Completion

Entropy ◽

10.3390/e23060771 ◽

2021 ◽

Vol 23 (6) ◽

pp. 771

Author(s):

Qiang Wei ◽

Guangmin Hu

Keyword(s):

Network Topology ◽

Real World ◽

State Of The Art ◽

The State ◽

Network Data ◽

Convolutional Network ◽

Deep Network ◽

Network Recovery ◽

Real World Datasets ◽

Node Labels

Collected network data are often incomplete, with both missing nodes and missing edges. Thus, network completion that infers the unobserved part of the network is essential for downstream tasks. Despite the emerging literature related to network recovery, the potential information has not been effectively exploited. In this paper, we propose a novel unified deep graph convolutional network that infers missing edges by leveraging node labels, features, and distances. Specifically, we first construct an estimated network topology for the unobserved part using node labels, then jointly refine the network topology and learn the edge likelihood with node labels, node features and distances. Extensive experiments using several real-world datasets show the superiority of our method compared with the state-of-the-art approaches.

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Privacy-preserving model learning on a blockchain network-of-networks

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocz214 ◽

2020 ◽

Vol 27 (3) ◽

pp. 343-354 ◽

Cited By ~ 9

Author(s):

Tsung-Ting Kuo ◽

Jihoon Kim ◽

Rodney A Gabriel

Keyword(s):

Network Topology ◽

Execution Time ◽

State Of The Art ◽

Privacy Preserving ◽

Consensus Algorithm ◽

Research Network ◽

Small Data ◽

Complex Data ◽

Model Learning ◽

Network Of Networks

Abstract Objective To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a “flattened” topology, while real-world research networks may consist of “network-of-networks” which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. Materials and Methods We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. Results HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. Discussion HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. Conclusion We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.

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Flat Tree Networks

ASME 2009 InterPACK Conference, Volume 1 ◽

10.1115/interpack2009-89336 ◽

2009 ◽

Author(s):

P. Khanduri ◽

A. Wood ◽

D. Cohen ◽

I. Birkeli ◽

F. Sem-Jacobsen

Keyword(s):

Network Topology ◽

State Of The Art ◽

Signal Integrity ◽

The State ◽

Hierarchical Network ◽

Switching Networks ◽

New Approach ◽

Tree Networks ◽

3 Dimensional ◽

Dimensional Array

Hierarchical switching networks connecting hundreds to thousands of servers are common in most large corporations. The state-of-the-art switches used in those networks are usually implemented by interconnecting a set of line cards that connect to hosts with a set of fabric cards that connect to other switches. This paper presents a new approach to implementing those switches using innovative packaging techniques that permit the 3-dimensional line/fabric card structure to be flattened into a 2-dimensional array of switch chips packaged on a PCB. The result of this flattening is a non-hierarchical network topology that we call a “flat tree”. This new approach reduces cost and complexity while improving performance and signal integrity.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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