145 research outputs found
Transit Node Routing Reconsidered
Transit Node Routing (TNR) is a fast and exact distance oracle for road
networks. We show several new results for TNR. First, we give a surprisingly
simple implementation fully based on Contraction Hierarchies that speeds up
preprocessing by an order of magnitude approaching the time for just finding a
CH (which alone has two orders of magnitude larger query time). We also develop
a very effective purely graph theoretical locality filter without any
compromise in query times. Finally, we show that a specialization to the online
many-to-one (or one-to-many) shortest path further speeds up query time by an
order of magnitude. This variant even has better query time than the fastest
known previous methods which need much more space.Comment: 19 pages, submitted to SEA'201
Lower Bounds in the Preprocessing and Query Phases of Routing Algorithms
In the last decade, there has been a substantial amount of research in
finding routing algorithms designed specifically to run on real-world graphs.
In 2010, Abraham et al. showed upper bounds on the query time in terms of a
graph's highway dimension and diameter for the current fastest routing
algorithms, including contraction hierarchies, transit node routing, and hub
labeling. In this paper, we show corresponding lower bounds for the same three
algorithms. We also show how to improve a result by Milosavljevic which lower
bounds the number of shortcuts added in the preprocessing stage for contraction
hierarchies. We relax the assumption of an optimal contraction order (which is
NP-hard to compute), allowing the result to be applicable to real-world
instances. Finally, we give a proof that optimal preprocessing for hub labeling
is NP-hard. Hardness of optimal preprocessing is known for most routing
algorithms, and was suspected to be true for hub labeling
Centrality scaling in large networks
Betweenness centrality lies at the core of both transport and structural
vulnerability properties of complex networks, however, it is computationally
costly, and its measurement for networks with millions of nodes is near
impossible. By introducing a multiscale decomposition of shortest paths, we
show that the contributions to betweenness coming from geodesics not longer
than L obey a characteristic scaling vs L, which can be used to predict the
distribution of the full centralities. The method is also illustrated on a
real-world social network of 5.5*10^6 nodes and 2.7*10^7 links
Trip-Based Public Transit Routing
We study the problem of computing all Pareto-optimal journeys in a public
transit network regarding the two criteria of arrival time and number of
transfers taken. We take a novel approach, focusing on trips and transfers
between them, allowing fine-grained modeling. Our experiments on the
metropolitan network of London show that the algorithm computes full 24-hour
profiles in 70 ms after a preprocessing phase of 30 s, allowing fast queries in
dynamic scenarios.Comment: Minor corrections, no substantial changes. To be presented at ESA
201
Tractable Pathfinding for the Stochastic On-Time Arrival Problem
We present a new and more efficient technique for computing the route that
maximizes the probability of on-time arrival in stochastic networks, also known
as the path-based stochastic on-time arrival (SOTA) problem. Our primary
contribution is a pathfinding algorithm that uses the solution to the
policy-based SOTA problem---which is of pseudo-polynomial-time complexity in
the time budget of the journey---as a search heuristic for the optimal path. In
particular, we show that this heuristic can be exceptionally efficient in
practice, effectively making it possible to solve the path-based SOTA problem
as quickly as the policy-based SOTA problem. Our secondary contribution is the
extension of policy-based preprocessing to path-based preprocessing for the
SOTA problem. In the process, we also introduce Arc-Potentials, a more
efficient generalization of Stochastic Arc-Flags that can be used for both
policy- and path-based SOTA. After developing the pathfinding and preprocessing
algorithms, we evaluate their performance on two different real-world networks.
To the best of our knowledge, these techniques provide the most efficient
computation strategy for the path-based SOTA problem for general probability
distributions, both with and without preprocessing.Comment: Submission accepted by the International Symposium on Experimental
Algorithms 2016 and published by Springer in the Lecture Notes in Computer
Science series on June 1, 2016. Includes typographical corrections and
modifications to pre-processing made after the initial submission to SODA'15
(July 7, 2014
TIGIT expressing CD4+T cells represent a tumor-supportive T cell subset in chronic lymphocytic leukemia
While research on T cell exhaustion in context of cancer particularly focuses on CD8C cytotoxic T cells, the
role of inhibitory receptors on CD4C T-helper cells have remained largely unexplored. TIGIT is a recently
identified inhibitory receptor on T cells and natural killer (NK) cells. In this study, we examined TIGIT
expression on T cell subsets from CLL patients. While we did not observe any differences in TIGIT expression
in CD8C T cells of healthy controls and CLL cells, we found an enrichment of TIGITC T cells in the CD4C T
cell compartment in CLL. Intriguingly, CLL patients with an advanced disease stage displayed elevated
numbers of CD4C TIGITC T cells compared to low risk patients. Autologous CLL-T cell co-culture assays
revealed that depleting CD4C TIGITC expressing T cells from co-cultures significantly decreased CLL viability.
Accordingly, a supportive effect of TIGITCCD4C T cells on CLL cells in vitro could be recapitulated by
blocking the interaction of TIGIT with its ligands using TIGIT-Fc molecules, which also impeded the T cell
specific production of CLL-prosurvival cytokines. Our data reveal that TIGITCCD4CT cells provide a
supportive microenvironment for CLL cells, representing a potential therapeutic target for CLL treatment
Separating Hierarchical and General Hub Labelings
In the context of distance oracles, a labeling algorithm computes vertex
labels during preprocessing. An query computes the corresponding distance
from the labels of and only, without looking at the input graph. Hub
labels is a class of labels that has been extensively studied. Performance of
the hub label query depends on the label size. Hierarchical labels are a
natural special kind of hub labels. These labels are related to other problems
and can be computed more efficiently. This brings up a natural question of the
quality of hierarchical labels. We show that there is a gap: optimal
hierarchical labels can be polynomially bigger than the general hub labels. To
prove this result, we give tight upper and lower bounds on the size of
hierarchical and general labels for hypercubes.Comment: 11 pages, minor corrections, MFCS 201
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