9,579 research outputs found
Accelerating Parallel Tempering: Quantile Tempering Algorithm (QuanTA)
Using MCMC to sample from a target distribution, on a
-dimensional state space can be a difficult and computationally expensive
problem. Particularly when the target exhibits multimodality, then the
traditional methods can fail to explore the entire state space and this results
in a bias sample output. Methods to overcome this issue include the parallel
tempering algorithm which utilises an augmented state space approach to help
the Markov chain traverse regions of low probability density and reach other
modes. This method suffers from the curse of dimensionality which dramatically
slows the transfer of mixing information from the auxiliary targets to the
target of interest as . This paper introduces a novel
prototype algorithm, QuanTA, that uses a Gaussian motivated transformation in
an attempt to accelerate the mixing through the temperature schedule of a
parallel tempering algorithm. This new algorithm is accompanied by a
comprehensive theoretical analysis quantifying the improved efficiency and
scalability of the approach; concluding that under weak regularity conditions
the new approach gives accelerated mixing through the temperature schedule.
Empirical evidence of the effectiveness of this new algorithm is illustrated on
canonical examples
A survey of self organisation in future cellular networks
This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks
Real time clustering of time series using triangular potentials
Motivated by the problem of computing investment portfolio weightings we
investigate various methods of clustering as alternatives to traditional
mean-variance approaches. Such methods can have significant benefits from a
practical point of view since they remove the need to invert a sample
covariance matrix, which can suffer from estimation error and will almost
certainly be non-stationary. The general idea is to find groups of assets which
share similar return characteristics over time and treat each group as a single
composite asset. We then apply inverse volatility weightings to these new
composite assets. In the course of our investigation we devise a method of
clustering based on triangular potentials and we present associated theoretical
results as well as various examples based on synthetic data.Comment: AIFU1
Recommended from our members
Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Neural Embeddings of Graphs in Hyperbolic Space
Neural embeddings have been used with great success in Natural Language
Processing (NLP). They provide compact representations that encapsulate word
similarity and attain state-of-the-art performance in a range of linguistic
tasks. The success of neural embeddings has prompted significant amounts of
research into applications in domains other than language. One such domain is
graph-structured data, where embeddings of vertices can be learned that
encapsulate vertex similarity and improve performance on tasks including edge
prediction and vertex labelling. For both NLP and graph based tasks, embeddings
have been learned in high-dimensional Euclidean spaces. However, recent work
has shown that the appropriate isometric space for embedding complex networks
is not the flat Euclidean space, but negatively curved, hyperbolic space. We
present a new concept that exploits these recent insights and propose learning
neural embeddings of graphs in hyperbolic space. We provide experimental
evidence that embedding graphs in their natural geometry significantly improves
performance on downstream tasks for several real-world public datasets.Comment: 7 pages, 5 figure
Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings
Buildings are key actors of the electrical gird. As such they have an important role to play in grid
stabilization, especially in a context where renewable energies are mandated to become an increasingly
important part of the energy mix. Demand response provides a mechanism to reduce or displace electrical
demand to better match electrical production. Buildings can be a pool of flexibility for the grid to operate
more efficiently. One of the ways to obtain flexibility from building managers and building users is the
introduction of variable energy prices which evolve depending on the expected load and energy generation.
In the proposed scenario, the wholesale energy price of electricity, a load prediction, and the elasticity of
consumers are used by an energy tariff emulator to predict prices to trigger end user flexibility. In this paper,
a cluster analysis to classify users is performed and an aggregated energy prediction is realised using Random
Forest machine learning algorithm.This paper is part of a project that has received funding
from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No
768614. This paper reflects only the author´s views and
neither the Agency nor the Commission are responsible
for any use that may be made of the information contained
therein
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