975 research outputs found
Accelerating sequential Monte Carlo with surrogate likelihoods
Delayed-acceptance is a technique for reducing computational effort for
Bayesian models with expensive likelihoods. Using a delayed-acceptance kernel
for Markov chain Monte Carlo can reduce the number of expensive likelihoods
evaluations required to approximate a posterior expectation. Delayed-acceptance
uses a surrogate, or approximate, likelihood to avoid evaluation of the
expensive likelihood when possible. Within the sequential Monte Carlo
framework, we utilise the history of the sampler to adaptively tune the
surrogate likelihood to yield better approximations of the expensive
likelihood, and use a surrogate first annealing schedule to further increase
computational efficiency. Moreover, we propose a framework for optimising
computation time whilst avoiding particle degeneracy, which encapsulates
existing strategies in the literature. Overall, we develop a novel algorithm
for computationally efficient SMC with expensive likelihood functions. The
method is applied to static Bayesian models, which we demonstrate on toy and
real examples, code for which is available at
https://github.com/bonStats/smcdar.Comment: 35 pages, 10 figure
Non-CO2 generating energy shares in the world : cross-country differences and polarization.
The aim of this paper is to examine the spatial distribution of non-CO2 generating energy sources in the world for the period 1990–2009, paying special attention to the evolution of cross-country disparities. To this end, after carrying out a classical convergence analysis, a more thorough investigation of the entire distribution is presented by examining its external shape, the intra-distribution dynamics and the long-run equilibrium distribution. This analysis reveals the existence of a weak, rather insignificant, convergence process and that large crosscountry differences are likely to persist in the long-run. Next, as polarization indicators are a proper way of appraising potential conflict in international environmental negotiations, we test whether, or not, the distribution dynamics concurs with the presence of polarization. Our results indicate that two poles can be clearly differentiated, one with high and other with low non-CO2 generating energy shares. In view of these findings, and to ensure a fair transition to a sustainable energy system, the paper calls for the development of an ambitious clean energy agenda, especially in countries with low non-CO2 generating energy shares
Evolutionary Inference from Admixed Genomes: Implications of Hybridization for Biodiversity Dynamics and Conservation
Hybridization as a macroevolutionary mechanism has been historically underappreciated among vertebrate biologists. Yet, the advent and subsequent proliferation of next-generation sequencing methods has increasingly shown hybridization to be a pervasive agent influencing evolution in many branches of the Tree of Life (to include ancestral hominids). Despite this, the dynamics of hybridization with regards to speciation and extinction remain poorly understood. To this end, I here examine the role of hybridization in the context of historical divergence and contemporary decline of several threatened and endangered North American taxa, with the goal to illuminate implications of hybridization for promoting—or impeding—population persistence in a shifting adaptive landscape.
Chapter I employed population genomic approaches to examine potential effects of habitat modification on species boundary stability in co-occurring endemic fishes of the Colorado River basin (Gila robusta and G. cypha). Results showed how one potential outcome of hybridization might drive species decline: via a breakdown in selection against interspecific heterozygotes and subsequent genetic erosion of parental species.
Chapter II explored long-term contributions of hybridization in an evolutionarily recent species complex (Gila) using a combination of phylogenomic and phylogeographic modelling approaches. Massively parallel computational methods were developed (and so deployed) to categorize sources of phylogenetic discordance as drivers of systematic bias among a panel of species tree inference algorithms. Contrary to past evidence, we found that hypotheses of hybrid origin (excluding one notable example) were instead explained by gene-tree discordance driven by a rapid radiation.
Chapter III examined patterns of local ancestry in the endangered red wolf genome (Canis rufus) – a controversial taxon of a long-standing debate about the origin of the species. Analyses show how pervasive autosomal introgression served to mask signatures of prior isolation—in turn misleading analyses that led the species to be interpreted as of recent hybrid origin. Analyses also showed how recombination interacts with selection to create a non-random, structured genomic landscape of ancestries with, in the case of the red wolf, the ‘original’ species tree being retained only in low-recombination ‘refugia’ of the X chromosome.
The final three chapters present bioinformatic software that I developed for my dissertation research to facilitate molecular approaches and analyses presented in Chapters I–III. Chapter IV details an in-silico method for optimizing similar genomic methods as used herein (RADseq of reduced representation libraries) for other non-model organisms. Chapter V describes a method for parsing genomic datasets for elements of interest, either as a filtering mechanism for downstream analysis, or as a precursor to targeted-enrichment reduced-representation genomic sequencing. Chapter VI presents a rapid algorithm for the definition of a ‘most parsimonious’ set of recombinational breakpoints in genomic datasets, as a method promoting local ancestry analyses as utilized in Chapter III.
My three case studies and accompanying software promote three trajectories in modern hybridization research: How does hybridization impact short-term population persistence? How does hybridization drive macroevolutionary trends? and How do outcomes of hybridization vary in the genome? In so doing, my research promotes a deeper understanding of the role that hybridization has and will continue to play in governing the evolutionary fates of lineages at both contemporary and historic timescales
Forecasting etfs- price movements using convolutional neural networks - methodology and comparison of industries - focus on industrials etf
The aim of this paper is to achieve two goals. Firstly, build and apply a convolutional neural
network to make predictions on historical data of the Vanguard Industrials ETF (VIS) in the
form of Buy, Hold and Sell signals. Secondly, making comparisons among different indus triesin order to derive potential performance deviations. By using three image encoding tech niques and a randomly generated model for comparison purposes, some promising results
have been achieved. Nevertheless, several classic strategies and the market performance
could not be beaten, mainly because model predictions for Buy and Sell signals showed
weaknesses
Learning Models for Discrete Optimization
We consider a class of optimization approaches that incorporate machine learning models into the algorithm structure. Our focus is on the algorithms that can learn the patterns in the search space in order to boost computational performance. The idea is to design optimization techniques that allow for computationally efficient tuning a priori. The final objective of this work is to provide efficient solvers that can be tuned for optimal performance in serial and parallel environments.This dissertation provides a novel machine learning model based on logistic regression and describes an implementation for scheduling problems. We incorporate the proposed learning model into a well-known optimization algorithm, tabu search, and demonstrate the potential of the underlying ideas. The dissertation also establishes a new framework for comparing optimization algorithms. This framework provides a comparison of algorithms that is statistically meaningful and intuitive. Using this framework, we demonstrate that the inclusion of the logistic regression model into the tabu search method provides significant boost of its performance. Finally, we study the parallel implementation of the algorithm and evaluate the algorithm performance when more connections between threads exist
Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain
The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio
Research And Application Of Parallel Computing Algorithms For Statistical Phylogenetic Inference
Estimating the evolutionary history of organisms, phylogenetic inference, is a
critical step in many analyses involving biological sequence data such as DNA.
The likelihood calculations at the heart of the most effective methods for
statistical phylogenetic analyses are extremely computationally intensive, and
hence these analyses become a bottleneck in many studies. Recent progress in
computer hardware, specifically the increase in pervasiveness of highly
parallel, many-core processors has created opportunities for new approaches to
computationally intensive methods, such as those in phylogenetic inference.
We have developed an open source library, BEAGLE, which uses parallel
computing methods to greatly accelerate statistical phylogenetic inference,
for both maximum likelihood and Bayesian approaches. BEAGLE defines a uniform
application programming interface and includes a collection of efficient
implementations that use NVIDIA CUDA, OpenCL, and C++ threading frameworks
for evaluating likelihoods under a wide variety of evolutionary models, on
GPUs as well as on multi-core CPUs. BEAGLE employs a number of different
parallelization techniques for phylogenetic inference, at different
granularity levels and for distinct processor architectures. On CUDA and
OpenCL devices, the library enables concurrent computation of site likelihoods,
data subsets, and independent subtrees. The general design features of the
library also provide a model for software development using parallel computing
frameworks that is applicable to other domains.
BEAGLE has been integrated with some of the leading programs in the field,
such as MrBayes and BEAST, and is used in a diverse range of evolutionary
studies, including those of disease causing viruses. The library can provide
significant performance gains, with the exact increase in performance
depending on the specific properties of the data set, evolutionary model, and
hardware. In general, nucleotide analyses are accelerated on the order of
10-fold and codon analyses on the order of 100-fold
An edge-driven multi-agent optimization model for infectious disease detection
This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, long-short term memory, and convolution neural networks, for accurately learning heterogeneous medical data in identifying disease infection. The multi-agent system is also consolidated for increasing the autonomy behaviours of the proposed framework, where each agent can easily share the derived learning outputs with the other agents in the system. Furthermore, evolutionary computation algorithms, such as memetic algorithms, and bee swarm optimization controlled the exploration of the hyper-optimization parameter space of the proposed framework. Intensive experimentation has been established on medical data. Strong results obtained confirm the superiority of our framework against the solutions that are state of the art, in both detection rate, and runtime performance, where the detection rate reaches 98% for handling real use cases.publishedVersio
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