Heritage, pride and place: exploring the contribution of World Heritage Site status to Liverpool’s sense of place and future development

The report considers opportunities and challenges for Liverpool to make the most of its World Heritage Site (WHS) designation, building on the methodologies applied within the Impacts 08 programme to assess the multiple impacts of large-scale cultural interventions. The analysis focuses primarily on the impact of the WHS designation on the image and reputation of Liverpool, as well as on local citizens’ sense of place. Whilst acknowledging findings from previous reports commissioned by English Heritage in relation to the possible impact of development on the Liverpool World Heritage Site’s ‘Outstanding Universal Value’, this study also explores the socio-cultural, economic and political impact of the designation and management of the WHS on the city and its residents

A comparison of AdaBoost algorithms for time series forecast combination

Recently, combination algorithms from machine learning classification have been extended to time series regression, most notably seven variants of the popular AdaBoost algorithm. Despite their theoretical promise their empirical accuracy in forecasting has not yet been assessed, either against each other or against any established approaches of forecast combination, model selection, or statistical benchmark algorithms. Also, none of the algorithms have been assessed on a representative set of empirical data, using only few synthetic time series. We remedy this omission by conducting a rigorous empirical evaluation using a representative set of 111 industry time series and a valid and reliable experimental design. We develop a full-factorial design over derived Boosting meta-parameters, creating 42 novel Boosting variants, and create a further 47 novel Boosting variants using research insights from forecast combination. Experiments show that only few Boosting meta-parameters increase accuracy, while meta-parameters derived from forecast combination research outperform others

Adaptive laser link reconfiguration using constraint propagation

This paper describes Harris AI research performed on the Adaptive Link Reconfiguration (ALR) study for Rome Lab, and focuses on the application of constraint propagation to the problem of link reconfiguration for the proposed space based Strategic Defense System (SDS) Brilliant Pebbles (BP) communications system. According to the concept of operations at the time of the study, laser communications will exist between BP's and to ground entry points. Long-term links typical of RF transmission will not exist. This study addressed an initial implementation of BP's based on the Global Protection Against Limited Strikes (GPALS) SDI mission. The number of satellites and rings studied was representative of this problem. An orbital dynamics program was used to generate line-of-site data for the modeled architecture. This was input into a discrete event simulation implemented in the Harris developed COnstraint Propagation Expert System (COPES) Shell, developed initially on the Rome Lab BM/C3 study. Using a model of the network and several heuristics, the COPES shell was used to develop the Heuristic Adaptive Link Ordering (HALO) Algorithm to rank and order potential laser links according to probability of communication. A reduced set of links based on this ranking would then be used by a routing algorithm to select the next hop. This paper includes an overview of Constraint Propagation as an Artificial Intelligence technique and its embodiment in the COPES shell. It describes the design and implementation of both the simulation of the GPALS BP network and the HALO algorithm in COPES. This is described using a 59 Data Flow Diagram, State Transition Diagrams, and Structured English PDL. It describes a laser communications model and the heuristics involved in rank-ordering the potential communication links. The generation of simulation data is described along with its interface via COPES to the Harris developed View Net graphical tool for visual analysis of communications networks. Conclusions are presented, including a graphical analysis of results depicting the ordered set of links versus the set of all possible links based on the computed Bit Error Rate (BER). Finally, future research is discussed which includes enhancements to the HALO algorithm, network simulation, and the addition of an intelligent routing algorithm for BP

Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting

This study explores both from a theoretical and empirical perspective how to model deterministic seasonality with artificial neural networks (ANN) to achieve the best forecasting accuracy. The aim of this study is to maximise the available seasonal information to the ANN while identifying the most economic form to code it; hence reducing the modelling degrees of freedom and simplifying the network’s training. An empirical evaluation on simulated and real data is performed and in agreement with the theoretical analysis no deseasonalising is required. A parsimonious coding based on seasonal indices is proposed that showed the best forecasting accuracy

Broadcasting Convolutional Network for Visual Relational Reasoning

In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features. BCN is a simple network module that collects effective spatial features, embeds location information and broadcasts them to the entire feature maps. We further introduce the Multi-Relational Network (multiRN) that improves the existing Relation Network (RN) by utilizing the BCN module. In pixel-based relation reasoning problems, with the help of BCN, multiRN extends the concept of `pairwise relations' in conventional RNs to `multiwise relations' by relating each object with multiple objects at once. This yields in O(n) complexity for n objects, which is a vast computational gain from RNs that take O(n^2). Through experiments, multiRN has achieved a state-of-the-art performance on CLEVR dataset, which proves the usability of BCN on relation reasoning problems.Comment: Accepted paper at ECCV 2018. 24 page

Advances in forecasting with artificial neural networks

There is decades long research interest in artificial neural networks (ANNs) that has led to several successful applications. In forecasting, both in theoretical and empirical works, ANNs have shown evidence of good performance, in many cases outperforming established benchmark models. However, our understanding of their inner workings is still limited, which makes it difficult for academicians and practitioners alike to use them. Furthermore, while there is a growing literature supporting their good performance in forecasting, there is also a lot of scepticism whether ANNs are able to provide reliable and robust forecasts. This analysis presents the advances of ANNs in the time series forecasting field, highlighting the current state of the art, which modelling issues have been solved and which are still critical for forecasting with ANNs, indicating future research directions

Cross-validation aggregation for combining autoregressive neural network forecasts

This paper evaluates kk-fold and Monte Carlo cross-validation and aggregation (crogging) for combining neural network autoregressive forecasts. We introduce Monte Carlo crogging which combines bootstrapping and cross-validation (CV) in a single approach through repeated random splitting of the original time series into mutually exclusive datasets for training. As the training/validation split is independent of the number of folds, the algorithm offers more flexibility in the size, and number of training samples compared to kk-fold cross-validation. The study also provides for crogging and bagging: (1) the first systematic evaluation across time series length and combination size, (2) a bias and variance decomposition of the forecast errors to understand improvement gains, and (3) a comparison to established benchmarks of model averaging and selection. Crogging can easily be extended to other autoregressive models. Results on real and simulated series demonstrate significant improvements in forecasting accuracy especially for short time series and long forecast horizons

Detecting a Z2Z_2 topologically ordered phase from unbiased infinite projected entangled-pair state simulations

We present an approach to identify topological order based on unbiased infinite projected entangled-pair states (iPEPS) simulations, i.e. where we do not impose a virtual symmetry on the tensors during the optimization of the tensor network ansatz. As an example we consider the ground state of the toric code model in a magnetic field exhibiting $Z_2$ topological order. The optimization is done by an efficient energy minimization approach based on a summation of tensor environments to compute the gradient. We show that the optimized tensors, when brought into the right gauge, are approximately $Z_2$ symmetric, and they can be fully symmetrized a posteriori to generate a stable topologically ordered state, yielding the correct topological entanglement entropy and modular S and U matrices. To compute the latter we develop a variant of the corner-transfer matrix method which is computationally more efficient than previous approaches based on the tensor renormalization group.Comment: 16 pages, 14 figure