1,791 research outputs found
Finding Multiple New Optimal Locations in a Road Network
We study the problem of optimal location querying for location based services
in road networks, which aims to find locations for new servers or facilities.
The existing optimal solutions on this problem consider only the cases with one
new server. When two or more new servers are to be set up, the problem with
minmax cost criteria, MinMax, becomes NP-hard. In this work we identify some
useful properties about the potential locations for the new servers, from which
we derive a novel algorithm for MinMax, and show that it is efficient when the
number of new servers is small. When the number of new servers is large, we
propose an efficient 3-approximate algorithm. We verify with experiments on
real road networks that our solutions are effective and attains significantly
better result quality compared to the existing greedy algorithms
Dependability in Aggregation by Averaging
Aggregation is an important building block of modern distributed
applications, allowing the determination of meaningful properties (e.g. network
size, total storage capacity, average load, majorities, etc.) that are used to
direct the execution of the system. However, the majority of the existing
aggregation algorithms exhibit relevant dependability issues, when prospecting
their use in real application environments. In this paper, we reveal some
dependability issues of aggregation algorithms based on iterative averaging
techniques, giving some directions to solve them. This class of algorithms is
considered robust (when compared to common tree-based approaches), being
independent from the used routing topology and providing an aggregation result
at all nodes. However, their robustness is strongly challenged and their
correctness often compromised, when changing the assumptions of their working
environment to more realistic ones. The correctness of this class of algorithms
relies on the maintenance of a fundamental invariant, commonly designated as
"mass conservation". We will argue that this main invariant is often broken in
practical settings, and that additional mechanisms and modifications are
required to maintain it, incurring in some degradation of the algorithms
performance. In particular, we discuss the behavior of three representative
algorithms Push-Sum Protocol, Push-Pull Gossip protocol and Distributed Random
Grouping under asynchronous and faulty (with message loss and node crashes)
environments. More specifically, we propose and evaluate two new versions of
the Push-Pull Gossip protocol, which solve its message interleaving problem
(evidenced even in a synchronous operation mode).Comment: 14 pages. Presented in Inforum 200
RankMerging: A supervised learning-to-rank framework to predict links in large social network
Uncovering unknown or missing links in social networks is a difficult task
because of their sparsity and because links may represent different types of
relationships, characterized by different structural patterns. In this paper,
we define a simple yet efficient supervised learning-to-rank framework, called
RankMerging, which aims at combining information provided by various
unsupervised rankings. We illustrate our method on three different kinds of
social networks and show that it substantially improves the performances of
unsupervised metrics of ranking. We also compare it to other combination
strategies based on standard methods. Finally, we explore various aspects of
RankMerging, such as feature selection and parameter estimation and discuss its
area of relevance: the prediction of an adjustable number of links on large
networks.Comment: 43 pages, published in Machine Learning Journa
On the energy efficiency-spectral efficiency trade-off in the uplink of CoMP system
In this paper, we derive a generic closed-form approximation (CFA) of the energy efficiency-spectral efficiency (EE-SE) trade-off for the uplink of coordinated multi-point (CoMP) system and demonstrate its accuracy for both idealistic and realistic power consumption models (PCMs). We utilize our CFA to compare CoMP against conventional non-cooperative system with orthogonal multiple access. In the idealistic PCM, CoMP is more energy efficient than non-cooperative system due to a reduction in power consumption; whereas in the realistic PCM, CoMP can also be more energy efficient but due to an improvement in SE and mainly for cell-edge communication and small cell deployment
On the Minimal Revision Problem of Specification Automata
As robots are being integrated into our daily lives, it becomes necessary to
provide guarantees on the safe and provably correct operation. Such guarantees
can be provided using automata theoretic task and mission planning where the
requirements are expressed as temporal logic specifications. However, in
real-life scenarios, it is to be expected that not all user task requirements
can be realized by the robot. In such cases, the robot must provide feedback to
the user on why it cannot accomplish a given task. Moreover, the robot should
indicate what tasks it can accomplish which are as "close" as possible to the
initial user intent. This paper establishes that the latter problem, which is
referred to as the minimal specification revision problem, is NP complete. A
heuristic algorithm is presented that can compute good approximations to the
Minimal Revision Problem (MRP) in polynomial time. The experimental study of
the algorithm demonstrates that in most problem instances the heuristic
algorithm actually returns the optimal solution. Finally, some cases where the
algorithm does not return the optimal solution are presented.Comment: 23 pages, 16 figures, 2 tables, International Joural of Robotics
Research 2014 Major Revision (submitted
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