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Search Results: 1 - 10 of 7672 matches for " Alex Rogers "
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Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries
James McInerney,Alex Rogers,Nicholas R. Jennings
Computer Science , 2013,
Abstract: In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted. We propose an alternative method of distribution (to standard road delivery) in which the existing mobility habits of a local population are leveraged to deliver aid, which raises two technical challenges in the areas optimisation and learning. For optimisation, a standard Markov decision process applied to this problem is intractable, so we provide an exact formulation that takes advantage of the periodicities in human location behaviour. To learn such behaviour models from sparse data (i.e., cell tower observations), we develop a Bayesian model of human mobility. Using real cell tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we find that our model outperforms the state of the art approaches in mobility prediction by at least 25% (in held-out data likelihood). Furthermore, when incorporating mobility prediction with our MDP approach, we find a 81.3% reduction in total delivery time versus routine planning that minimises just the number of participants in the solution path.
A comparison of non-intrusive load monitoring methods for commercial and residential buildings
Nipun Batra,Oliver Parson,Mario Berges,Amarjeet Singh,Alex Rogers
Computer Science , 2014,
Abstract: Non intrusive load monitoring (NILM), or energy disaggregation, is the process of separating the total electricity consumption of a building as measured at single point into the building's constituent loads. Previous research in the field has mostly focused on residential buildings, and although the potential benefits of applying this technology to commercial buildings have been recognised since the field's conception, NILM in the commercial domain has been largely unexplored by the academic community. As a result of the heterogeneity of this section of the building stock (i.e., encompassing buildings as diverse as airports, malls and coffee shops), and hence the loads within them, many of the solutions developed for residential energy disaggregation do not apply directly. In this paper we highlight some insights for NILM in the commercial domain using data collected from a large smart meter deployment within an educational campus in Delhi, India, of which a subset of the data has been released for public use. We present an empirical characterisation of loads in commercial buildings, highlighting the differences in energy consumption and load characteristics between residential and commercial buildings. We assess the validity of the assumptions generally made by NILM solutions for residential buildings when applied to measurements from commercial facilities. Based on our observations, we discuss the required traits for a NILM system for commercial buildings, and run benchmark residential NILM algorithms on our data set to confirm our observations. To advance the research in commercial buildings energy disaggregation, we release a subset of our data set, called COMBED (commercial building energy data set).
Efficient State-Space Inference of Periodic Latent Force Models
Steven Reece,Stephen Roberts,Siddhartha Ghosh,Alex Rogers,Nicholas Jennings
Statistics , 2013,
Abstract: Latent force models (LFM) are principled approaches to incorporating solutions to differential equations within non-parametric inference methods. Unfortunately, the development and application of LFMs can be inhibited by their computational cost, especially when closed-form solutions for the LFM are unavailable, as is the case in many real world problems where these latent forces exhibit periodic behaviour. Given this, we develop a new sparse representation of LFMs which considerably improves their computational efficiency, as well as broadening their applicability, in a principled way, to domains with periodic or near periodic latent forces. Our approach uses a linear basis model to approximate one generative model for each periodic force. We assume that the latent forces are generated from Gaussian process priors and develop a linear basis model which fully expresses these priors. We apply our approach to model the thermal dynamics of domestic buildings and show that it is effective at predicting day-ahead temperatures within the homes. We also apply our approach within queueing theory in which quasi-periodic arrival rates are modelled as latent forces. In both cases, we demonstrate that our approach can be implemented efficiently using state-space methods which encode the linear dynamic systems via LFMs. Further, we show that state estimates obtained using periodic latent force models can reduce the root mean squared error to 17% of that from non-periodic models and 27% of the nearest rival approach which is the resonator model.
Molecular Diversity of Fungal Phylotypes Co-Amplified Alongside Nematodes from Coastal and Deep-Sea Marine Environments
Punyasloke Bhadury, Holly Bik, John D. Lambshead, Melanie C. Austen, Gary R. Smerdon, Alex D. Rogers
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0026445
Abstract: Nematodes and fungi are both ubiquitous in marine environments, yet few studies have investigated relationships between these two groups. Microbial species share many well-documented interactions with both free-living and parasitic nematode species, and limited data from previous studies have suggested ecological associations between fungi and nematodes in benthic marine habitats. This study aimed to further document the taxonomy and distribution of fungal taxa often co-amplified from nematode specimens. A total of 15 fungal 18S rRNA phylotypes were isolated from nematode specimens representing both deep-sea and shallow water habitats; all fungal isolates displayed high pairwise sequence identities with published data in Genbank (99–100%) and unpublished high-throughput 454 environmental datasets (>95%). BLAST matches indicate marine fungal sequences amplified in this study broadly represent taxa within the phyla Ascomycota and Basidiomycota, and several phylotypes showed robust groupings with known taxa in phylogenetic topologies. In addition, some fungal phylotypes appeared to be present in disparate geographic habitats, suggesting cosmopolitan distributions or closely related species complexes in at least some marine fungi. The present study was only able to isolate fungal DNA from a restricted set of nematode taxa; further work is needed to fully investigate the taxonomic scope and function of nematode-fungal interactions.
Bounding the Estimation Error of Sampling-based Shapley Value Approximation
Sasan Maleki,Long Tran-Thanh,Greg Hines,Talal Rahwan,Alex Rogers
Computer Science , 2013,
Abstract: The Shapley value is arguably the most central normative solution concept in cooperative game theory. It specifies a unique way in which the reward from cooperation can be "fairly" divided among players. While it has a wide range of real world applications, its use is in many cases hampered by the hardness of its computation. A number of researchers have tackled this problem by (i) focusing on classes of games where the Shapley value can be computed efficiently, or (ii) proposing representation formalisms that facilitate such efficient computation, or (iii) approximating the Shapley value in certain classes of games. For the classical \textit{characteristic function} representation, the only attempt to approximate the Shapley value for the general class of games is due to Castro \textit{et al.} \cite{castro}. While this algorithm provides a bound on the approximation error, this bound is \textit{asymptotic}, meaning that it only holds when the number of samples increases to infinity. On the other hand, when a finite number of samples is drawn, an unquantifiable error is introduced, meaning that the bound no longer holds. With this in mind, we provide non-asymptotic bounds on the estimation error for two cases: where (i) the \textit{variance}, and (ii) the \textit{range}, of the players' marginal contributions is known. Furthermore, for the second case, we show that when the range is significantly large relative to the Shapley value, the bound can be improved (from $O(\frac{r}{m})$ to $O(\sqrt{\frac{r}{m}})$). Finally, we propose, and demonstrate the effectiveness of using stratified sampling for improving the bounds further.
Knapsack based Optimal Policies for Budget-Limited Multi-Armed Bandits
Long Tran-Thanh,Archie Chapman,Alex Rogers,Nicholas R. Jennings
Computer Science , 2012,
Abstract: In budget-limited multi-armed bandit (MAB) problems, the learner's actions are costly and constrained by a fixed budget. Consequently, an optimal exploitation policy may not be to pull the optimal arm repeatedly, as is the case in other variants of MAB, but rather to pull the sequence of different arms that maximises the agent's total reward within the budget. This difference from existing MABs means that new approaches to maximising the total reward are required. Given this, we develop two pulling policies, namely: (i) KUBE; and (ii) fractional KUBE. Whereas the former provides better performance up to 40% in our experimental settings, the latter is computationally less expensive. We also prove logarithmic upper bounds for the regret of both policies, and show that these bounds are asymptotically optimal (i.e. they only differ from the best possible regret by a constant factor).
Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets
Jack Kelly,Nipun Batra,Oliver Parson,Haimonti Dutta,William Knottenbelt,Alex Rogers,Amarjeet Singh,Mani Srivastava
Computer Science , 2014, DOI: 10.1145/2674061.2675024
Abstract: In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances. The toolkit contains: a number of importers for existing public data sets, a set of preprocessing and statistics functions, a benchmark disaggregation algorithm and a set of metrics to evaluate the performance of such algorithms. Specifically, this release of the toolkit has been designed to enable the use of large data sets by only loading individual chunks of the whole data set into memory at once for processing, before combining the results of each chunk.
NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
Nipun Batra,Jack Kelly,Oliver Parson,Haimonti Dutta,William Knottenbelt,Alex Rogers,Amarjeet Singh,Mani Srivastava
Statistics , 2014, DOI: 10.1145/2602044.2602051
Abstract: Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.
A Two-Step Growth Curve: Approach to the von Bertalanffy and Gompertz Equations  [PDF]
Laura Rogers-Bennett, Donald W. Rogers
Advances in Pure Mathematics (APM) , 2016, DOI: 10.4236/apm.2016.65023
Abstract: Many curves have been proposed and debated to model individual growth of marine invertebrates. Broadly, they fall into two classes, first order (e.g. von Bertalanffy) and sigmoidal (e.g. Gompertz). We provide an innovative approach which demonstrates that the growth curves are not mutually exclusive but that either may arise from a simple three-stage growth model \"\" with two steps (k1 and k2) depending on the ratio of the growth parameters \"\". The new approach predicts sigmoidal growth when \"\" is close to 1, but if either growth from stage A to stage B or B to C is fast relative to the other, the slower of the two steps becomes the growth limiting step and the model reduces to first order growth. The resulting curves indicate that there is a substantial difference in the estimated size at time t during the period of active growth. This novel two-step rate model generates a growth surface that allows for changes in the rate parameters over time as reflected in the new parameter n(t) = k1(t) -?k2(t). The added degree of freedom brings about individual growth trajectories across the growth surface that is not easily mapped using conventional growth modeling techniques. This two (or more) stage growth model yields a growth surface that allows for a wide range of growth trajectories, accommodating staged growth, growth lags, as well as indeterminate growth and can help resolve debates as to which growth curves should be used to model animal growth. This flexibility can improve estimates of growth parameters used in population models influencing model outcomes and ultimately management decisions.=
New insights on the pathogenesis of pyloric stenosis of infancy. A review with emphasis on the hyperacidity theory  [PDF]
Ian M. Rogers
Open Journal of Pediatrics (OJPed) , 2012, DOI: 10.4236/ojped.2012.22017
Abstract: A review is presented on the theories concerning the cause of pyloric stenosis with emphasis on the primary position of inherited hyperacidity in pathogenesis. Existing theories are critically analysed and the hyperacidity theory is precisely defined in the light of recent physiological insights into the gastrointestinal hormone motilin. The progressive fixed fasting hypergastrinaemia within the first few weeks of life will, in the baby who inherits acid secretion at the top of the normal range, produce hyperacidity of sufficient severity to trigger the process of acid-induced work hypertrophy of the pylorus. The potential contribution of motilin is discussed. The baby who inherits a normal gastric acidity will not reach acid levels severe enough to trigger sphincter hypertrophy despite the early gastrin stimulus. The potential threat will cease when gastrin naturally declines with age and the pyloric canal becomes wider. Genetic factors clearly must also be involved and these are separately discussed.
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