56,660 research outputs found
Graph-based Semi-Supervised & Active Learning for Edge Flows
We present a graph-based semi-supervised learning (SSL) method for learning
edge flows defined on a graph. Specifically, given flow measurements on a
subset of edges, we want to predict the flows on the remaining edges. To this
end, we develop a computational framework that imposes certain constraints on
the overall flows, such as (approximate) flow conservation. These constraints
render our approach different from classical graph-based SSL for vertex labels,
which posits that tightly connected nodes share similar labels and leverages
the graph structure accordingly to extrapolate from a few vertex labels to the
unlabeled vertices. We derive bounds for our method's reconstruction error and
demonstrate its strong performance on synthetic and real-world flow networks
from transportation, physical infrastructure, and the Web. Furthermore, we
provide two active learning algorithms for selecting informative edges on which
to measure flow, which has applications for optimal sensor deployment. The
first strategy selects edges to minimize the reconstruction error bound and
works well on flows that are approximately divergence-free. The second approach
clusters the graph and selects bottleneck edges that cross cluster-boundaries,
which works well on flows with global trends
Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization
Statistical traffic data analysis is a hot topic in traffic management and
control. In this field, current research progresses focus on analyzing traffic
flows of individual links or local regions in a transportation network. Less
attention are paid to the global view of traffic states over the entire
network, which is important for modeling large-scale traffic scenes. Our aim is
precisely to propose a new methodology for extracting spatio-temporal traffic
patterns, ultimately for modeling large-scale traffic dynamics, and long-term
traffic forecasting. We attack this issue by utilizing Locality-Preserving
Non-negative Matrix Factorization (LPNMF) to derive low-dimensional
representation of network-level traffic states. Clustering is performed on the
compact LPNMF projections to unveil typical spatial patterns and temporal
dynamics of network-level traffic states. We have tested the proposed method on
simulated traffic data generated for a large-scale road network, and reported
experimental results validate the ability of our approach for extracting
meaningful large-scale space-time traffic patterns. Furthermore, the derived
clustering results provide an intuitive understanding of spatial-temporal
characteristics of traffic flows in the large-scale network, and a basis for
potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013
Recent Developments in Bulgarian Transport Privatization Policy
Expanding Bulgaria’s political, economic, and cultural cooperation with the countries of Asia is a major priority of the Bulgarian government policy. Transport plays a key role in the implementation of this priority both by providing the necessary conditions for international transit traffic and by meeting the needs of the Bulgarian economy and population. Structural reform in transport to a great extent depends on a sustainable investment policy. At present, prevailing conditions are likely to attract investments, especially to the airports of Sofia and Bourgas. In recent years, the Bulgarian State Railways (“BDZh”) has lagged behind in its development in comparison with the other transport modes in the country and the railways in other European countries. The rehabilitation of the railways is crucial not only for BDZh itself but also for the entire country because the railways are the backbone of the international transport corridors that cross Bulgaria. The management of the Ministry of Transport considers privatization a significant element of the structural reform in the branch.
The introduction details how expanding Bulgaria\u27s political, economic, and cultural cooperation with the countries of Asia is a major priority of the Bulgarian government policy, and how transport plays a key role in the implementation of this priority both by providing the necessary conditions for international transit traffic and by meeting the needs of the Bulgarian economy and population. Part I addresses how structural reform in transport to a great extent depends on a sustainable investment policy. Part II focuses on the opportunities which investment in airports present. Part III addresses the advantages of investment in ports. Part IV focusses on Bulgarian State Railways. Finally, Part V addresses how privatization intersects with each of these transport sectors and is a significant element of structural reform
Recent Developments in Bulgarian Transport Privatization Policy
Expanding Bulgaria’s political, economic, and cultural cooperation with the countries of Asia is a major priority of the Bulgarian government policy. Transport plays a key role in the implementation of this priority both by providing the necessary conditions for international transit traffic and by meeting the needs of the Bulgarian economy and population. Structural reform in transport to a great extent depends on a sustainable investment policy. At present, prevailing conditions are likely to attract investments, especially to the airports of Sofia and Bourgas. In recent years, the Bulgarian State Railways (“BDZh”) has lagged behind in its development in comparison with the other transport modes in the country and the railways in other European countries. The rehabilitation of the railways is crucial not only for BDZh itself but also for the entire country because the railways are the backbone of the international transport corridors that cross Bulgaria. The management of the Ministry of Transport considers privatization a significant element of the structural reform in the branch.
The introduction details how expanding Bulgaria\u27s political, economic, and cultural cooperation with the countries of Asia is a major priority of the Bulgarian government policy, and how transport plays a key role in the implementation of this priority both by providing the necessary conditions for international transit traffic and by meeting the needs of the Bulgarian economy and population. Part I addresses how structural reform in transport to a great extent depends on a sustainable investment policy. Part II focuses on the opportunities which investment in airports present. Part III addresses the advantages of investment in ports. Part IV focusses on Bulgarian State Railways. Finally, Part V addresses how privatization intersects with each of these transport sectors and is a significant element of structural reform
Supersampling and network reconstruction of urban mobility
Understanding human mobility is of vital importance for urban planning,
epidemiology, and many other fields that aim to draw policies from the
activities of humans in space. Despite recent availability of large scale data
sets related to human mobility such as GPS traces, mobile phone data, etc., it
is still true that such data sets represent a subsample of the population of
interest, and then might give an incomplete picture of the entire population in
question. Notwithstanding the abundant usage of such inherently limited data
sets, the impact of sampling biases on mobility patterns is unclear -- we do
not have methods available to reliably infer mobility information from a
limited data set. Here, we investigate the effects of sampling using a data set
of millions of taxi movements in New York City. On the one hand, we show that
mobility patterns are highly stable once an appropriate simple rescaling is
applied to the data, implying negligible loss of information due to subsampling
over long time scales. On the other hand, contrasting an appropriate null model
on the weighted network of vehicle flows reveals distinctive features which
need to be accounted for. Accordingly, we formulate a "supersampling"
methodology which allows us to reliably extrapolate mobility data from a
reduced sample and propose a number of network-based metrics to reliably assess
its quality (and that of other human mobility models). Our approach provides a
well founded way to exploit temporal patterns to save effort in recording
mobility data, and opens the possibility to scale up data from limited records
when information on the full system is needed.Comment: 14 pages, 4 figure
Topology Discovery of Sparse Random Graphs With Few Participants
We consider the task of topology discovery of sparse random graphs using
end-to-end random measurements (e.g., delay) between a subset of nodes,
referred to as the participants. The rest of the nodes are hidden, and do not
provide any information for topology discovery. We consider topology discovery
under two routing models: (a) the participants exchange messages along the
shortest paths and obtain end-to-end measurements, and (b) additionally, the
participants exchange messages along the second shortest path. For scenario
(a), our proposed algorithm results in a sub-linear edit-distance guarantee
using a sub-linear number of uniformly selected participants. For scenario (b),
we obtain a much stronger result, and show that we can achieve consistent
reconstruction when a sub-linear number of uniformly selected nodes
participate. This implies that accurate discovery of sparse random graphs is
tractable using an extremely small number of participants. We finally obtain a
lower bound on the number of participants required by any algorithm to
reconstruct the original random graph up to a given edit distance. We also
demonstrate that while consistent discovery is tractable for sparse random
graphs using a small number of participants, in general, there are graphs which
cannot be discovered by any algorithm even with a significant number of
participants, and with the availability of end-to-end information along all the
paths between the participants.Comment: A shorter version appears in ACM SIGMETRICS 2011. This version is
scheduled to appear in J. on Random Structures and Algorithm
A numerical method for junctions in networks of shallow-water channels
There is growing interest in developing mathematical models and appropriate
numerical methods for problems involving networks formed by, essentially,
one-dimensional (1D) domains joined by junctions. Examples include hyperbolic
equations in networks of gas tubes, water channels and vessel networks for
blood and lymph in the human circulatory system. A key point in designing
numerical methods for such applications is the treatment of junctions, i.e.
points at which two or more 1D domains converge and where the flow exhibits
multidimensional behaviour. This paper focuses on the design of methods for
networks of water channels. Our methods adopt the finite volume approach to
make full use of the two-dimensional shallow water equations on the true
physical domain, locally at junctions, while solving the usual one-dimensional
shallow water equations away from the junctions. In addition to mass
conservation, our methods enforce conservation of momentum at junctions; the
latter seems to be the missing element in methods currently available. Apart
from simplicity and robustness, the salient feature of the proposed methods is
their ability to successfully deal with transcritical and supercritical flows
at junctions, a property not enjoyed by existing published methodologies.
Systematic assessment of the proposed methods for a variety of flow
configurations is carried out. The methods are directly applicable to other
systems, provided the multidimensional versions of the 1D equations are
available
Dark clouds on the horizon:the challenge of cloud forensics
We introduce the challenges to digital forensics introduced by the advent and adoption of technologies, such as encryption, secure networking, secure processors and anonymous routing. All potentially render current approaches to digital forensic investigation unusable. We explain how the Cloud, due to its global distribution and multi-jurisdictional nature, exacerbates these challenges. The latest developments in the computing milieu threaten a complete “evidence blackout” with severe implications for the detection, investigation and prosecution of cybercrime. In this paper, we review the current landscape of cloud-based forensics investigations. We posit a number of potential solutions. Cloud forensic difficulties can only be addressed if we acknowledge its socio-technological nature, and design solutions that address both human and technological dimensions. No firm conclusion is drawn; rather the objective is to present a position paper, which will stimulate debate in the area and move the discipline of digital cloud forensics forward. Thus, the paper concludes with an invitation to further informed debate on this issue
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