J.A. Quinn, A. Andama, I. Munabi, F.N. Kiwanuka. Automated Blood Smear Analysis for Mobile Malaria Diagnosis. Chapter to appear in Mobile Point-of-Care Monitors and Diagnostic Device Design, eds. W. Karlen and K. Iniewski, CRC Press.
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J.A. Quinn, V. Frias-Martinez, L. Subramanian. Computational Sustainability and Artificial Intelligence in the Developing World. To appear in Artificial Intelligence Magazine.

S. Liu, J.A. Quinn, M.U. Gutmann, T. Suzuki, M. Sugiyama. Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation, to appear in Neural Computation, 2014.

J.A. Quinn, M. Sugiyama. A Least-Squares Approach to Anomaly Detection in Static and Sequential Data. Pattern Recognition Letters 40:36-40, 2014.
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R. Ssekibuule, J.A. Quinn, K Leyton-Brown. A Mobile Market for Agricultural Trade in Uganda. The Fourth Annual Symposium on Computing for Development (ACM DEV), 2013.
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J.A. Quinn. Computational Techniques for Crop Disease Monitoring in the Developing World. Invited paper in The Twelfth International Symposium on Intelligent Data Analysis, Advances in Intelligent Data Analysis (12) 13-18, 2013, Springer LNCS.
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S. Liu, J.A. Quinn, M.U. Gutmann, M. Sugiyama. Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013), 2013.
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M. Mubangizi, C. Ikae, A. Spiliopoulou, J.A. Quinn. Coupling Spatiotemporal Disease Modeling with Diagnosis. Conference of the Association for the Advancement of Artificial Intelligence (AAAI), 2012.
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J.A. Quinn and C.K.I. Williams. Physiological Monitoring with Factorial Switching Linear Dynamical Systems, chapter in Bayesian Time Series Models, eds. D. Barber, T. Cemgil, S. Chiappa, Cambridge University Press, 2011.
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J.A. Quinn, J. Mooij, T. Heskes, M. Biehl. Learning of Causal Relations, Invited paper in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2011.
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E. Mwebaze, M. Biehl, J.A. Quinn. Causal Relevance Learning for Robust Classification under Interventions, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2011.
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J.A. Quinn, K. Leyton-Brown, E. Mwebaze. Modeling and Monitoring Crop Disease in Developing Countries. Conference of the Association for the Advancement of Artificial Intelligence (AAAI), 2011.
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E. Mwebaze, P. Schneider, F.-M. Schleif, J.R. Aduwo, J.A. Quinn, S. Haase, T. Villmann, M. Biehl. Divergence based classification in Learning Vector Quantization, Neurocomputing 74(9):1429-1435, 2011.

J.A. Quinn, W. Okori and A. Gidudu. Increased-Specificity Famine Prediction using Satellite Observation Data, First Annual Symposium on Computing for Development (ACM DEV), London, 2010.
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J.R. Aduwo, E. Mwebaze and J.A. Quinn. Automated Vision-Based Diagnosis of Cassava Mosaic Disease, Workshop on Data Mining in Agriculture (DMA 2010), Berlin, 2010.
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E. Mwebaze and J.A. Quinn. Finding Predictive Relationships Between Notifiable Diseases with Markov Blanket Discovery, Int Conf Computing and ICT Research, Kampala, (local conference) 2010.

J.A. Quinn and R. Nakibuule. Traffic Flow Monitoring in Crowded Cities, AAAI Spring Symposium on Artificial Intelligence for Development, Stanford, 2010.
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E. Mwebaze, W. Okori and J.A. Quinn. Causal Structure Learning for Famine Prediction, AAAI Spring Symposium on Artificial Intelligence for Development, Stanford, 2010.
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J.A. Quinn, C.K.I. Williams and N. McIntosh. Factorial Switching Linear Dynamical Systems applied to Physiological Monitoring, IEEE Transactions on Pattern Analysis and Machine Intelligence 31(9):1537-51, 2009.
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M. Mubangizi, E. Mwebaze and J.A. Quinn. Computational Prediction of Cholera Outbreaks, Int Conf Computing and ICT Research, Kampala, (local conference) 2009.
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N. Eagle, J.A. Quinn and A. Clauset. Methodologies for Continuous Cellular Tower Data Analysis, Seventh International Conference on Pervasive Computing, 2009.
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N. Eagle, A. Clauset. and J.A. Quinn. Location Segmentation, Inference and Prediction for Anticipatory Computing, AAAI Spring Symposium on Technosocial Predictive Analytics, Stanford, 2009.
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E. Mwebaze and J.A. Quinn. Fast Committee-Based Structure Learning, NIPS Workshop on Causality, 2008. Honourable mention at the workshop challenge for "significant advance on the REGED dataset".
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J.A. Quinn and C.K.I. Williams. Signal Masking in Gaussian Channels, IEEE International Conference on Acoustics, Speech and Signal Processing, 2008.
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C. Fox and J. Quinn. How to be Lost: Principled Pruning and Priming with Particles for Score Following, Proc International Computer Music Conference (ICMC) 2007.
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J.A. Quinn. Bayesian Condition Monitoring in Neonatal Intensive Care. PhD thesis, University of Edinburgh, 2007.
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J.A. Quinn and C.K.I. Williams. Known Unknowns: Novelty Detection in Condition Monitoring, Invited paper in Proc 3rd Iberian Conference on Pattern Recognition and Image Analysis, 2007, Springer LNCS.
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C.K.I. Williams, J.A. Quinn, N. McIntosh. Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care, Advances in Neural Information Processing Systems 18, 2006, MIT Press.
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A.S. Law, Y. Freer, J.R.W. Hunter, R.H. Logie, N. McIntosh, J.A. Quinn. A Comparison of Graphical and Textual Presentations of Time Series Data to Support Medical Decision Making in the Neonatal Intensive Care Unit, Journal of Clinical Monitoring and Computing 19;183-194, 2005.
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Other things

Interview on BBC Radio Scotland about computerised neonatal monitoring, May 2007.
mp3 (4:30 mins, 4Mb).