10,402 research outputs found
Hierarchical self-organization of non-cooperating individuals
Hierarchy is one of the most conspicuous features of numerous natural,
technological and social systems. The underlying structures are typically
complex and their most relevant organizational principle is the ordering of the
ties among the units they are made of according to a network displaying
hierarchical features. In spite of the abundant presence of hierarchy no
quantitative theoretical interpretation of the origins of a multi-level,
knowledge-based social network exists. Here we introduce an approach which is
capable of reproducing the emergence of a multi-levelled network structure
based on the plausible assumption that the individuals (representing the nodes
of the network) can make the right estimate about the state of their changing
environment to a varying degree. Our model accounts for a fundamental feature
of knowledge-based organizations: the less capable individuals tend to follow
those who are better at solving the problems they all face. We find that
relatively simple rules lead to hierarchical self-organization and the specific
structures we obtain possess the two, perhaps most important features of
complex systems: a simultaneous presence of adaptability and stability. In
addition, the performance (success score) of the emerging networks is
significantly higher than the average expected score of the individuals without
letting them copy the decisions of the others. The results of our calculations
are in agreement with a related experiment and can be useful from the point of
designing the optimal conditions for constructing a given complex social
structure as well as understanding the hierarchical organization of such
biological structures of major importance as the regulatory pathways or the
dynamics of neural networks.Comment: Supplementary videos are to be found at
http://hal.elte.hu/~nepusz/research/supplementary/hierarchy
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Energy management in communication networks: a journey through modelling and optimization glasses
The widespread proliferation of Internet and wireless applications has
produced a significant increase of ICT energy footprint. As a response, in the
last five years, significant efforts have been undertaken to include
energy-awareness into network management. Several green networking frameworks
have been proposed by carefully managing the network routing and the power
state of network devices.
Even though approaches proposed differ based on network technologies and
sleep modes of nodes and interfaces, they all aim at tailoring the active
network resources to the varying traffic needs in order to minimize energy
consumption. From a modeling point of view, this has several commonalities with
classical network design and routing problems, even if with different
objectives and in a dynamic context.
With most researchers focused on addressing the complex and crucial
technological aspects of green networking schemes, there has been so far little
attention on understanding the modeling similarities and differences of
proposed solutions. This paper fills the gap surveying the literature with
optimization modeling glasses, following a tutorial approach that guides
through the different components of the models with a unified symbolism. A
detailed classification of the previous work based on the modeling issues
included is also proposed
Distance Oracles for Time-Dependent Networks
We present the first approximate distance oracle for sparse directed networks
with time-dependent arc-travel-times determined by continuous, piecewise
linear, positive functions possessing the FIFO property.
Our approach precomputes approximate distance summaries from
selected landmark vertices to all other vertices in the network. Our oracle
uses subquadratic space and time preprocessing, and provides two sublinear-time
query algorithms that deliver constant and approximate
shortest-travel-times, respectively, for arbitrary origin-destination pairs in
the network, for any constant . Our oracle is based only on
the sparsity of the network, along with two quite natural assumptions about
travel-time functions which allow the smooth transition towards asymmetric and
time-dependent distance metrics.Comment: A preliminary version appeared as Technical Report ECOMPASS-TR-025 of
EU funded research project eCOMPASS (http://www.ecompass-project.eu/). An
extended abstract also appeared in the 41st International Colloquium on
Automata, Languages, and Programming (ICALP 2014, track-A
Self-Organization of Topographic Mixture Networks Using Attentional Feedback
This paper proposes a biologically-motivated neural network model of supervised learning. The model possesses two novel learning mechanisms. The first is a network for learning topographic mixtures. The network's internal category nodes are the mixture components, which learn to encode smooth distributions in the input space by taking advantage of topography in the input feature maps. The second mechanism is an attentional biasing feedback circuit. When the network makes an incorrect output prediction, this feedback circuit modulates the learning rates of the category nodes, by amounts based on the sharpness of their tuning, in order to improve the network's prediction accuracy. The network is evaluated on several standard classification benchmarks and shown to perform well in comparison to other classifiers. Possible relationships are discussed between the network's learning properties and those of biological neural networks. Possible future extensions of the network are also discussed.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409
An investigation of shortest paths algorithms
In this work, we classify the shortest path problems, review all source algorithms and analyse the different implementations of single source algorithms using various list structures and labelling techniques. Furthermore, we study the Sensitivity Analysis of one-to-all problems and present an algorithm, Senet, for their Post Optimality Analysis. Senet determines all the critical values for the weight of an arc (which could be optimal, non-optimal or non-existant) at which the optimal solution changes. Senet also provides the updated optimal solution for every range formed by two successive critical values
- …