4,602 research outputs found
Statistical performance analysis with dynamic workload using S-NET
Volkmar Wieser, Philip K. F. Hölzenspies, Michael Roßbory, and Raimund Kirner, 'Statistical performance analysis with dynamic workload using S-NET'. Paper presented at the Workshop on Feedback-Directed Compiler Optimization for Multi-Core Architectures. Paris, France 23-25 January 2012In this paper the ADVANCE approach for engineering con- current software systems with well-balanced hardware ef- ficiency is adressed using the stream processing language S-Net. To obtain the cost information in the concurrent system the metrics throughput, latency, and jitter are evalu- ated by analyzing generated synthetical data as well as using an industrial related application in the future. As fall-out an Eclipse plugin for S-Net has been developed to provide sup- port for syntax highlighting, content assistance, hover help, and more, for easier and faster development. The presented results of the current work are on the one hand an indicator for the status quo of the ADVANCE vision and on the other hand used to improve the applied statistical analysis tech- niques within ADVANCE. Like the ADVANCE project, this work is still under development, but further improvements and speedups are expected in the near future
A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation
A software platform for global optimisation, called PaGMO, has been developed
within the Advanced Concepts Team (ACT) at the European Space Agency, and was
recently released as an open-source project. PaGMO is built to tackle
high-dimensional global optimisation problems, and it has been successfully
used to find solutions to real-life engineering problems among which the
preliminary design of interplanetary spacecraft trajectories - both chemical
(including multiple flybys and deep-space maneuvers) and low-thrust (limited,
at the moment, to single phase trajectories), the inverse design of
nano-structured radiators and the design of non-reactive controllers for
planetary rovers. Featuring an arsenal of global and local optimisation
algorithms (including genetic algorithms, differential evolution, simulated
annealing, particle swarm optimisation, compass search, improved harmony
search, and various interfaces to libraries for local optimisation such as
SNOPT, IPOPT, GSL and NLopt), PaGMO is at its core a C++ library which employs
an object-oriented architecture providing a clean and easily-extensible
optimisation framework. Adoption of multi-threaded programming ensures the
efficient exploitation of modern multi-core architectures and allows for a
straightforward implementation of the island model paradigm, in which multiple
populations of candidate solutions asynchronously exchange information in order
to speed-up and improve the optimisation process. In addition to the C++
interface, PaGMO's capabilities are exposed to the high-level language Python,
so that it is possible to easily use PaGMO in an interactive session and take
advantage of the numerous scientific Python libraries available.Comment: To be presented at 'ICATT 2010: International Conference on
Astrodynamics Tools and Techniques
Corporate payments networks and credit risk rating
Aggregate and systemic risk in complex systems are emergent phenomena
depending on two properties: the idiosyncratic risks of the elements and the
topology of the network of interactions among them. While a significant
attention has been given to aggregate risk assessment and risk propagation once
the above two properties are given, less is known about how the risk is
distributed in the network and its relations with the topology. We study this
problem by investigating a large proprietary dataset of payments among 2.4M
Italian firms, whose credit risk rating is known. We document significant
correlations between local topological properties of a node (firm) and its
risk. Moreover we show the existence of an homophily of risk, i.e. the tendency
of firms with similar risk profile to be statistically more connected among
themselves. This effect is observed when considering both pairs of firms and
communities or hierarchies identified in the network. We leverage this
knowledge to show the predictability of the missing rating of a firm using only
the network properties of the associated node
Random Neural Networks and Optimisation
In this thesis we introduce new models and learning algorithms for the Random
Neural Network (RNN), and we develop RNN-based and other approaches for the
solution of emergency management optimisation problems.
With respect to RNN developments, two novel supervised learning algorithms are
proposed. The first, is a gradient descent algorithm for an RNN extension model
that we have introduced, the RNN with synchronised interactions (RNNSI), which
was inspired from the synchronised firing activity observed in brain neural circuits.
The second algorithm is based on modelling the signal-flow equations in RNN as a
nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory
quasi-Newton algorithm specifically designed for the RNN case.
Regarding the investigation of emergency management optimisation problems,
we examine combinatorial assignment problems that require fast, distributed and
close to optimal solution, under information uncertainty. We consider three different
problems with the above characteristics associated with the assignment of
emergency units to incidents with injured civilians (AEUI), the assignment of assets
to tasks under execution uncertainty (ATAU), and the deployment of a robotic
network to establish communication with trapped civilians (DRNCTC).
AEUI is solved by training an RNN tool with instances of the optimisation problem
and then using the trained RNN for decision making; training is achieved using
the developed learning algorithms. For the solution of ATAU problem, we introduce
two different approaches. The first is based on mapping parameters of the
optimisation problem to RNN parameters, and the second on solving a sequence of
minimum cost flow problems on appropriately constructed networks with estimated
arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer
linear programming formulation, which is based on network flows. Finally, we design
and implement distributed heuristic algorithms for the deployment of robots
when the civilian locations are known or uncertain
Line Graphs of Weighted Networks for Overlapping Communities
In this paper, we develop the idea to partition the edges of a weighted graph
in order to uncover overlapping communities of its nodes. Our approach is based
on the construction of different types of weighted line graphs, i.e. graphs
whose nodes are the links of the original graph, that encapsulate differently
the relations between the edges. Weighted line graphs are argued to provide an
alternative, valuable representation of the system's topology, and are shown to
have important applications in community detection, as the usual node partition
of a line graph naturally leads to an edge partition of the original graph.
This identification allows us to use traditional partitioning methods in order
to address the long-standing problem of the detection of overlapping
communities. We apply it to the analysis of different social and geographical
networks.Comment: 8 Pages. New title and text revisions to emphasise differences from
earlier paper
Local Descriptors Optimized for Average Precision
Extraction of local feature descriptors is a vital stage in the solution
pipelines for numerous computer vision tasks. Learning-based approaches improve
performance in certain tasks, but still cannot replace handcrafted features in
general. In this paper, we improve the learning of local feature descriptors by
optimizing the performance of descriptor matching, which is a common stage that
follows descriptor extraction in local feature based pipelines, and can be
formulated as nearest neighbor retrieval. Specifically, we directly optimize a
ranking-based retrieval performance metric, Average Precision, using deep
neural networks. This general-purpose solution can also be viewed as a listwise
learning to rank approach, which is advantageous compared to recent local
ranking approaches. On standard benchmarks, descriptors learned with our
formulation achieve state-of-the-art results in patch verification, patch
retrieval, and image matching.Comment: 13 pages, 8 figures. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
- …