Bivariate normal, conditional and rectangular probabilities: A computer program with applications

Some results for the bivariate normal distribution analysis are presented. Computer programs for conditional normal probabilities, marginal probabilities, as well as joint probabilities for rectangular regions are given: routines for computing fractile points and distribution functions are also presented. Some examples from a closed circuit television experiment are included

Deployment of churn prediction model in financial services industry

© 2016 IEEE. Nowadays, data analytics techniques are playing an increasingly crucial role in financial services due to the huge benefits they bring. To ensure a successful implementation of an analytics project, various factors and procedures need to be considered besides technical issues. This paper introduces some practical lessons from our deployment of a data analytics project in a leading wealth management company in Australia. Specifically, the process of building a customer churn prediction model is described. Besides common steps of data analysis, how to deal with other practical issues like data privacy and change management that are encountered by many financial companies are also introduced

A Multiple Source based Transfer Learning Framework for Marketing Campaigns

© 2018 IEEE. The rapid growing number of marketing campaigns demands an efficient learning model to identify prospective customers to target. Transfer learning is widely considered as a major way to improve the learning performance by using the generated knowledge from previous learning tasks. Most recent studies focused on transferring knowledge from source domains to target domains which may result in knowledge missing. To avoid this, we proposed a multiple source based transfer learning framework to do it reversely. The data in target domains is transferred into source domains by normalizing them into the same distributions and then improving the learning task in target domains by its generated knowledge in source domains. The proposed method is general and can deal with supervised and unsupervised inductive and transductive learning simultaneously with a compatibility to work with different machine learning models. The experiments on real-world campaign data demonstrate the performance of the proposed method

Combining heterogeneous features for time series prediction

© 2017 IEEE. Time series prediction is a challenging task in reality, and various methods have been proposed for it. However, only the historical series of values are exploited in most of existing methods. Therefore, the predictive models might be not effective in some cases, due to: (1) the historical series of values is not sufficient usually, and (2) features from heterogeneous sources such as the intrinsic features of data samples themselves, which could be very useful, are not take into consideration. To address these issues, we proposed a novel method in this paper which learns the predictive model based on the combination of dynamic features extracted from series of historical values and static features of data samples. To evaluate the performance of our proposed method, we compare it with linear regression and boosted trees, and the experimental results validate our method's superiority

Should the fiscal powers of the Northern Ireland Assembly be enhanced?

<p>Should the fiscal powers of the Northern Ireland Assembly be enhanced? <i>Regional Studies</i>. Northern Ireland has been characterized by an inability to narrow the persistent economic gap relative to Britain. Some commentators have suggested that regional corporation tax variation may be the ‘game changer’ in closing this gap. This paper draws on a range of studies that help one better understand the historical and institutional context. However, the analysis of tax variation is broader than this. Consideration is given as to which taxes might be the most suitable candidates for devolution. While greater tax variations could certainly complement an emphasis on increased competitiveness aimed at improving economic outcomes, they are no substitute for such a focus. As is often the case in institutional and economic development, issues of sequencing and policy capacity are salient.</p

Cost-sensitive churn prediction in fund management services

© Springer International Publishing AG, part of Springer Nature 2018. Churn prediction is vital to companies as to identify potential churners and prevent losses in advance. Although it has been addressed as a classification task and a variety of models have been employed in practice, fund management services have presented several special challenges. One is that financial data is extremely imbalanced since only a tiny proportion of customers leave every year. Another is a unique cost-sensitive learning problem, i.e., costs of wrong predictions for churners should be related to their account balances, while costs of wrong predictions for non-churners should be the same. To address these issues, this paper proposes a new churn prediction model based on ensemble learning. In our model, multiple classifiers are built using sampled datasets to tackle the imbalanced data issue while exploiting data fully. Moreover, a novel sampling strategy is proposed to deal with the unique cost-sensitive issue. This model has been deployed in one of the leading fund management institutions in Australia, and its effectiveness has been fully validated in real applications

SunbYte: an autonomous pointing framework for low-cost robotic solar telescopes on high altitude balloons

The design and usability of a fully autonomous robotic control system (SunbYte - Sheffield University Balloon “lYfted” TElescope) for solar tracking and observational applications onboard high-altitude balloons are addressed here. The design is based on a six-step development plan balancing scientific objectives and practical engineering requirements. The high-altitude solar observational system includes low-cost components such as a Cassegrain-type telescope, stepper motors, harmonic drives, USB cameras and microprocessors. OpenCV installed from ROS (Robotic Operating System), python and C facilitated the collection, compression, and processing of housekeeping and scientific data. This processed data was then transmitted to the ground station through the launch vehicle’s telecommunication link. The SunbYte system allows the brightest spot in the sky, the sun, to be identified, and a telescope pointed towards it with high enough accuracy that a scientific camera can capture images. This paper gathers and presents the results from primarily two missions with the High-Altitude Student Platform (HASP, NASA Balloon Program office and LaSpace). Additionally, a discussion will be made comparing these with an earlier iteration flown with the German-Swedish “REXUS/BEXUS” programme coordinated by the European Space Agency. By capturing and analysing a series of tracking images with the location of the Sun at the calibrated centre, the system demonstrated the tracking capabilities on an unstable balloon during ascent. Housekeeping sensor data was collected to further analyse the thermal and mechanical performance. The low temperature increased friction in the drive train and reduced the responsiveness of the harmonic drive actuation system. This caused some issues which require further work in future missions, for example, with SunbYte 4 and its work when flying with the HEMERA ZPB (Zero Pressure Balloon) program

An ontological approach to creating an Andean Weaving Knowledge Base

Andean textiles are products of one of the richest, oldest and continuous weaving traditions in the world. Understanding the knowledge and practice of textile production as a form of cultural heritage is particularly relevant in the Andean context due to erosion of clothing traditions, reuse of traditional textiles on commodities targeted at the tourism market, and loss of knowledge embedded in textile production. ``Weaving Communities of Practice'' was a pilot project that aimed to create a knowledge base of Andean weaving designed to contribute to curatorial practice and heritage policy. The research team gathered data on the chain of activities, instruments, resources, peoples, places and knowledge involved in the production of textiles, relating to over 700 textile samples. A major part of the project has been the modelling and representation of the knowledge of domain experts and information about the textile objects themselves in the form of an OWL ontology, and the development of a suite of search facilities to be supported by the ontology. This paper describes the research challenges faced in developing the ontology and search facilities, the methodology adopted, the design and implementation of the system, and the design and outcomes of a user evaluation of the system undertaken with a group of domain experts

In vivo formation of natural HgSe nanoparticles in the liver and brain of pilot whales

Acknowledgments Z.G. thanks to the College of Physical Sciences at University of Aberdeen and Chevron USA for the provided studentship. P.M.K. is the recipient of an Australian Research Council Future Fellowship (FT120100277). Parts of this research were undertaken on the XFM beamline at the Australian Synchrotron, Victoria, Australia. The assistance of Daryl Howard (XFM beamline, Australian Synchrotron) is acknowledged. Although EPA contributed to this article, the research presented was not performed by or funded by EPA and was not subject to EPA's quality system requirements. Consequently, the views, interpretations, and conclusions expressed in this article are solely those of the authors and do not necessarily reflect or represent EPA's views or policies. MRCAT operations are supported by the Department of Energy and the MRCAT member institutions. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. The authors declare no competing financial interests. F.L.R. and A.B. acknowledge Scottish Marine Animal Stranding Scheme and Marine Scotland for funding. Author Contributions E.M.K and J.F. designed the experiments. Z.G. measured total Hg and conducted Hg speciation. Total Se was determined by A.R. and Z.G. M.M.L. performed Se speciation and 2D imaging by LA-ICP-MS was done by D.S.U. XANES was performed by K.S. and XRF by E.L. and P.M.K. Samples were obtained by E.M.K. through A.B. and age determination was done by F.R. spICP-MS was performed by E.H.L., K.L., G.W. and Z.G. The manuscript was written by Z.G. and all authors discussed the results and commented on the manuscript.Peer reviewedPublisher PD

Methane mole fraction and δ13^{13}C above and below the trade wind inversion at Ascension Island in air sampled by aerial robotics

The Authors.Ascension Island is a remote South Atlantic equatorial site, ideal for monitoring tropical background CH4. In September 2014 and July 2015, octocopters were used to collect air samples in Tedlar bags from different heights above and below the well-defined Trade Wind Inversion (TWI), sampling a maximum altitude of 2700 m above mean sea level. Sampling captured both remote air in the marine boundary layer below the TWI and also air masses above the TWI that had been lofted by convective systems in the African tropics. Air above the TWI was characterized by higher CH$_{4}$, but no distinct shift in δ$^{13}$C was observed compared to the air below. Back trajectories indicate that lofted CH$_{4}$ emissions from Southern Hemisphere Africa have bulk δ$^{13}$C$_{CH4}$ signatures similar to background, suggesting mixed emissions from wetlands, agriculture, and biomass burning. The campaigns illustrate the usefulness of unmanned aerial system sampling and Ascension's value for atmospheric measurement in an understudied region.This work was part of the investigation of the southern methane anomaly: causes, implications, and relevance to past global events funded by the UK Natural Environment Research Council (NERC) (grant NE/K006045/1) and NERC studentship. Data will be deposited in the UK Centre for Environmental Data Analysis on completion of Rebecca Brownlow’s PhD thesis