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  • max little

    • publications: databases

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      publications: full list

      [1] Z. Xue, H. Lu, T. Zhang, and Max A. Little. Patient-specific game-based transfer method for Parkinson's disease severity prediction. Artificial Intelligence in Medicine, 150:102810--http, 2024. [ http ]
      [2] Adam Farooq, Yordan P. Raykov, Petar Raykov, and Max A. Little. Adaptive latent feature sharing for piecewise linear dimensionality reduction. Journal of Machine Learning Research, pages in--press, 2024. [ http ]
      [3] Max A. Little, Xi He, and Ugur Kayas. Dynamic programming by polymorphic algebraic shortcut fusion. Formal Aspects of Computing, 1:in--press, 2024. [ http ]
      [4] D. Cakiqi and M.A. Little. Algorithmic syntactic causal identification. arXiv, 2024. [ http ]
      [5] Kars Veldkamp, Luc Evers, Jordan Raykov, Max A. Little, Marc Brouwer, Bas Bloem, and Jos Thannhauser. Real-life heart rate assessment: a digital biomarker for autonomic dysfunction in early Parkinson’s disease? In MDS Congress 2023, 2023.
      [6] Roberta Terranova, Luc Evers, Diogo Soriano, Jordan Raykov, Max A. Little, Hayriye Cagnan, Bas Bloem, and Rick Helmich. Real-life monitoring of Parkinson’s disease tremor: does the non-tremor subtype really exist? In MDS Congress 2023, 2023.
      [7] X. He and M.A. Little. E01Loss: A Python library for solving the exact 0-1 loss linear classification problem, 2023. [ http ]
      [8] C. Marra, T. Chico, A. Alexandrow, W. G. Dixon, N. Briffa, E. Rainaldi, M.A. Little, K. Size, A. Tsanas, J.B. Franklin, et al. The Power of Digital Health Technologies for Patient Registries: Addressing Remaining Challenges. NPJ Digital Medicine, pages under--review, 2023.
      [9] Xi He, Waheed Ul Rahman, and Max A. Little. An efficient, provably exact, practical algorithm for the 0-1 loss linear classification problem. arXiv, pages https--arXiv, 2023.
      [10] Luc J.W. Evers, Yordan P. Raykov, Tom M. Heskes, Jesse H. Krijthe, Bastiaan R. Bloem, and Max A. Little. Continuous monitoring of Parkinson tremor in real-life: a prototypical network approach. arXiv, 2023.
      [11] M.A. Little. GRAPL: A computational library for nonparametric structural causal modelling, analysis and inference. Journal of Open Source Software, 7(76):4534, 2022.
      [12] Tao Zhang, Hao-Ran Shan, and Max A Little. Causal GraphSAGE: A robust graph method for classification based on causal sampling. Pattern Recognition, 128:108696, 2022.
      [13] Dhurim Cakiqi and Max A. Little. Non-probabilistic Markov categories for causal modeling in machine learning. In Applied Category Theory 2022 (ACT2022), 2022. [ .pdf ]
      [14] K. Claes, V. Ticcinelli, R. Badawy, Y.P. Raykov, L.J.W. Evers, and M.A. Little. TSDF: A simple yet comprehensive, unified data storage and exchange format standard for digital biosensor data in health applications. arXiv, 2022.
      [15] Yordan P. Raykov, Luc J.W. Evers, Reham Badawy, Bastiaan Bloem, Tom M. Heskes, Marjan Meinders, Kasper Claes, and Max A. Little. Probabilistic modelling of gait for robust passive monitoring in daily life. IEEE Journal of Biomedical and Health Informatics, 25(6):2293--2304, 2021.
      [16] Max A. Little. Smartphones for remote symptom monitoring of Parkinson’s disease. Journal of Parkinson's Disease, 11(s1):S49--S53, 2021.
      [17] Amir Hossein Poorjam, Mathew Shaji Kavalekalam, Liming Shi, Yordan P Raykov, Jesper Rindom Jensen, Max A Little, and Mads Græsbøll Christensen. Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson's Disease Detection. Speech Communication, 127:1--16, 2021.
      [18] Athanasios Tsanas, Lorraine Ramig, and Max A. Little. Remote assessment of Parkinson’s disease symptom severity using the simulated cellular mobile telephone network. IEEE Access, pages 1--13, 2021.
      [19] Y. Qarout, Yordan P. Raykov, and Max A. Little. Few-shot time series segmentation using prototype-defined infinite hidden Markov models. arXiv, pages arXiv--2102, 2021.
      [20] A Farooq, Yordan P. Raykov, P. Raykov, and Max A. Little. Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction. arXiv, pages arxiv--2006, 2021.
      [21] A Pearlmutter, J Nantes, N Giladi, F Horak, R Alcalay, J Hausdorff, T Simuni, MA Little, A Jha, S Bozzi, et al. Clinical Trial Digital Endpoint Development: Patient & Provider Perspectives on Most Impactful Functional Aspects of PD. In EUROPEAN JOURNAL OF NEUROLOGY, volume 28, pages 837--837. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2021.
      [22] Larsson Omberg, Elias Chaibub Neto, Thanneer M. Perumal, Abhishek Pratap, Aryton Tediarjo, Jamie Adams, Bastiaan R. Bloem, Brian M. Bot, Molly Elson, Samuel M. Goldman, et al. Remote smartphone monitoring of Parkinson’s disease and individual response to therapy. Nature Biotechnology, pages 1546--1696, 2021.
      [23] Yazan Qarout, Yordan P Raykov, and Max A Little. Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities. Engineering Proceedings, 6(1):35, 2021.
      [24] Max A. Little, Sami Volotinen, Brad Sanderson, Ulla Huopaniemi, Florence D. Mowlem, Jennifer Olt, and Bill Byrom. Novel algorithms deriving clinical performance measures from smartphone sensor data collected under a walking test. bioRxiv, pages BIORXIV--2021, 2021.
      [25] Wasifur Rahman, Sangwu Lee, Md Saiful Islam, Victor Nikhil Antony, Harshil Ratnu, Mohammad Rafayet Ali, Abdullah Al Mamun, Ellen Wagner, Stella Jensen-Roberts, Emma Waddell, et al. Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study. Journal of Medical Internet Research, 23(10):e26305, 2021.
      [26] Yazan Qarout, Yordan P. Raykov, and Max A. Little. Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities. Sensors, 20(3), 2020.
      [27] Liming Shi, Jesper Kjær Nielsen, Jesper Rindom Jensen, Max A Little, and Mads Græsbøll Christensen. Instantaneous Bayesian Pitch Tracking in Colored Noise. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020.
      [28] E. Ray Dorsey, Larsson Omberg, Emma Waddell, Jamie L. Adams, Roy Adams, Mohammad Rafayet Ali, Katherine Amodeo, Abigail Arky, Erika F. Augustine, Karthik Dinesh, et al. Deep Phenotyping of Parkinson’s Disease. Journal of Parkinson's Disease, 10(3):855--873, 2020.
      [29] Adam Farooq, Yordan P Raykov, Petar Raykov, and Max A Little. LATENT FEATURE SHARING: AN ADAPTIVE APPROACH TO LINEAR DECOMPOSITION MODELS. arXiv preprint arXiv:2006.12369, 2020.
      [30] Luc JW Evers, Yordan P Raykov, Jesse H Krijthe, Ana Lígia Silva de Lima, Reham Badawy, Kasper Claes, Tom M Heskes, Max A Little, Marjan J Meinders, and Bastiaan R Bloem. Real-life gait performance as a digital biomarker for motor fluctuations: the Parkinson@Home validation study. Journal of Medical Internet Research, 22(10):e19068, 2020.
      [31] Wasifur Rahman, Sangwu Lee, Md. Saiful Islam, Victor Nikhil Antony, Harshil Ratnu, Mohammad Rafayet Ali, Abdullah Al Mamun, Ellen Wagner, Stella Jensen-Roberts, Max A. Little, et al. Detecting Parkinson’s Disease From an Online Speech-Task. arXiv, pages arxiv--2009, 2020.
      [32] Alexander Oldroyd, Belay Yimer, Max Little, William Dixon, and Hector Chinoy. Daily myositis symptom changes collected via a smartphone-based app are associated with flare occurrence-providing evidence of potential digital biomarkers. In ARTHRITIS & RHEUMATOLOGY, volume 72. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2020.
      [33] Andrea Sturchio, Luca Marsili, Joaquin A. Vizcarra, Alok K. Dwivedi, Marcelo A. Kauffman, Andrew P. Duker, Peixin Lu, Michael W. Pauciulo, Benjamin D. Wissel, Emily J. Hill, Benjamin Stecher, Elizabeth G. Keeling, Achala S. Vagal, Lily Wang, David B. Haslam, Matthew J. Robson, Caroline M. Tanner, Daniel W. Hagey, Samir El Andaloussi, Kariem Ezzat, Ronan M. T. Fleming, Long J. Lu, Max A. Little, and Alberto J. Espay. Phenotype-Agnostic Molecular Subtyping of Neurodegenerative Disorders: The Cincinnati Cohort Biomarker Program (CCBP). Frontiers in Aging Neuroscience, 12, 2020. [ http ]
      [34] Max A Little, M. Zizi, I. Clarysse, B. Burg, and Abel Villca Roque. Methods and apparatus for self-calibrating non-invasive cuffless blood pressure measurements, October 2019. US Patent US20190307337A1.
      [35] Jacqueline M Lane, Samuel Jones, Hassan Dashti, Andrew Wood, Krishna Aragam, Vincent T van Hees, Ben Brumpton, Bendik Slagsvold Winsvold, Heming Wang, Jack Bowden, et al. Biological and clinical insights from genetics of insomnia symptoms. Nature Genetics, 51:387--393, 2019.
      [36] Max A. Little. Machine Learning for Signal Processing, 2019.
      [37] A.L Beukenhorst, M.J. Parkes, L. Cook, R. Barnard, S.N. van der Veer, M.A. Little, K. Howells, C. Sanders, J.C. Sergeant, T.W. O'Neill, et al. Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study. JMIR Research Protocols, 8(1), 2019.
      [38] Hassan S Dashti, Samuel E Jones, Andrew R Wood, Jacqueline M Lane, Vincent T. van Hees, Heming Wang, Jessica A Rhodes, Yanwei Song, Krunal Patel, Simon G Anderson, et al. Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. Nature Communications, 10:1100, 2019.
      [39] Amir Hossein Poorjam Alavijeh, Max A Little, Jesper Rindom Jensen, and Mads Græsbøll Christensen. Quality Control in Remote Speech Data Collection. IEEE Journal of Selected Topics in Signal Processing, 13(2):236--243, 2019.
      [40] Luc Evers, Jordan Raykov, Marjan Meinders, Tom Heskes, Kasper Claes, Reham Badawy, Bas Bloem, and Max A. Little. Real-life gait performance as a marker for motor fluctuations: the Parkinson@home validation study. Movement Disorders, 2019.
      [41] H Wang, J.M. Lane, S.E. Jones, H.S. Dashti, H. Ollila, A.R. Wood, V.T. van Hees, B. Brumpton, B.S. Winsvold, K. Kantojarvi, et al. Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes. Nature Communications, 10(1):3503, 2019.
      [42] Liming Shi, Jesper Kjær Nielsen, Jesper Rindom Jensen, Max A. Little, and Mads Græsbøll Christensen. Robust Bayesian Pitch Tracking Based on the Harmonic Model. IEEE Transactions on Audio, Speech and Language Processing, 27(9):1737--1751, 2019.
      [43] Alexander Oldroyd, Max A. Little, William Dixon, and Hector Chinoy. A review of accelerometer-derived physical activity in the idiopathic inflammatory myopathies. BMC Rheumatology, 3:41, 2019.
      [44] Reham Badawy, Farhan Hameed, Lauren Bataille, Max A. Little, Kasper Claes, Suchi Saria, Jesse M. Cedarbaum, Diane Stephenson, Jon Neville, Walter Maetzler, et al. Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research. Digital Biomarkers, 3:116--132, 2019.
      [45] Max A. Little and Reham Badawy. Causal bootstrapping. arXiv preprint, pages arXiv--1910, 2019.
      [46] Max A. Little. Mathematical foundations. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [47] Max A. Little. Optimization. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [48] Max A. Little. Random sampling. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [49] Max A. Little. Probabilistic graphical models. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [50] Max A. Little. Statistical machine learning. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [51] Max A. Little. Linear-Gaussian systems and signal processing. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [52] Max A. Little. Discrete signals: sampling, quantization and coding. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [53] Max A. Little. Nonlinear and non-Gaussian signal processing. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [54] Max A. Little. Nonparametric Bayesian machine learning and signal processing. In Machine Learning for Signal Processing. Oxford University Press, 2019.
      [55] A.H. Poorjam, M.A. Little, J.R. Jensen, and M. G. Christensen. A parametric approach for classification of distortions in pathological voices. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
      [56] A.H. Poorjam, M.A. Little, J.R. Jensen, and M. G. Christensen. A supervised approach to global signal-to-noise ratio estimation for whispered and pathological voices. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
      [57] Andong Zhan, Srihari Mohan, Christopher Tarolli, Ruth B. Schneider, Jamie L. Adams, Saloni Sharma, Molly J. Elson, Kelsey L. Spear, Alistair M. Glidden, Max A. Little, et al. Using smartphones and machine learning to quantify Parkinson disease severity: The mobile Parkinson disease score. JAMA Neurology, 75(7):876--880, 2018.
      [58] Ana Lígia Silva de Lima, Luc JW Evers, Tim Hahn, Nienke M de Vries, Margaret Daeschler, Babak Boroojerdi, Dolors Terricabras, Max A. Little, Bastiaan R Bloem, and Marjan J Faber. Impact of motor fluctuations on real-life gait in Parkinson's patients. Gait & Posture, 62:388--394, 2018.
      [59] Reham Badawy, Yordan P. Raykov, Luc J.W. Evers, Bastiaan R. Bloem, Marjan J. Faber, Andong Zhan, Kasper Claes, and Max A. Little. Automated quality control for sensor based symptom measurement performed outside the lab. Sensors, 18(4):1215, 2018.
      [60] AL Beukenhorst, JC Sergeant, MA Little, J McBeth, and WG Dixon. Consumer Smartwatches for Collecting Self-Report and Sensor Data: App Design and Engagement. Studies in Health Technology and Informatics, 247:291--295, 2018.
      [61] Siddharth Arora, Fahd Baig, Christine Lo, Thomas R. Barber, Michael A. Lawton, Andong Zhan, Michal Rolinski, Claudio Ruffmann, Johannes C. Klein, Jane Rumbold, et al. Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD. Neurology, 91(16):e1528--e1538, 2018.
      [62] Siddharth Arora, Naomi P. Visanji, Tiago A. Mestre, Athanasios Tsanas, Amaal AlDakheel, Barbara S. Connolly, Carmen Gasca-Salas, Drew S. Kern, Jennifer Jain, Elizabeth J. Slow, et al. Investigating Voice as a Biomarker for LRRK2-Associated Parkinson’s Disease. Journal of Parkinson's Disease, 8(4):503--510, 2018.
      [63] Liming Shi, Jesper Kjær Nielsen, Jesper Rindom Jensen, and Max A Little. Bayesian Pitch Tracking Based on the Harmonic Model. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018.
      [64] Adam Farooq, Yordan Raykov, Luc Evers, and Max A. Little. Adaptive Probabilistic Principal Components Analysis. In NIPS 2018 Workshop: All of Bayesian Nonparametrics, pages https--drive, 2018.
      [65] Yordan Raykov, Luc Evers, Reham Badawy, Marjan Faber, Bastiaan Bloem, and Max A Little. Probabilistic modelling of gait for remote passive monitoring applications. In NIPS 2018 Workshop: ML4Health, pages https--arxiv, 2018.
      [66] C Lo, S Arora, F Baig, T Barber, M Lawton, A Zhan, M.A. Little, and M Hu. The use of smartphone task derived features to predict clinical scores in Parkinson's Disease (PD). In MOVEMENT DISORDERS, volume 33, pages S511--S511. WILEY, 2018.
      [67] Jacqueline M Lane, Jingjing Liang, Irma Vlasac, Simon G Anderson, David A Bechtold, Jack Bowden, Richard Emsley, Shubhroz Gill, Max A Little, AnneMarie I Luik, et al. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nature Genetics, 49:274--281, 2017.
      [68] Ana L. S. de Lima, Luc J. W. Evers, T. Hahn, Lauren Bataille, Jamie L. Hamilton, Max A. Little, Yasuyuki Okuma, Bastiaan R. Bloem, and Marjan J. Faber. Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review. Journal of Neurology, 264(8):1642--1654, 2017.
      [69] Max A Little, Gael Varoquaux, Sohrab Saeb, Luca Lonini, Arun Jayaraman, David C Mohr, and Konrad P Kording. Using and understanding cross-validation strategies. Perspectives on Saeb et al. GigaScience, 6(5):gix020, 2017.
      [70] Amir Hossein Poorjam, Jesper Rindom Jensen, Max A. Little, and Mads Græsbøll Christensen. Dominant Distortion Classification for Pre-Processing of Vowels in Remote Biomedical Voice Analysis. In Interspeech 2017: Conference of the International Speech Communication Association, 2017.
      [71] Liming Shi, Jesper K. Nielsen, Max A. Little, and Mads G. Christensen. A Kalman-Based Fundamental Frequency Estimation Algorithm. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) 2017, 2017.
      [72] F. Lipsmeier, G. Fernandez, D. Wolf, T. Kilchenmann, A. Scotland, J. Schjodt-Eriksen, W. Cheng, J. Siebourg-Polster, J. Liping, J. Soto, et al. Remote, high-frequency monitoring of motor symptoms in early-stage Parkinson's disease patients in the phase I RG7935/PRX002 clinical trial. Neurodegenerative Diseases, 17(suppl. 1):1--1890, 2017.
      [73] Simon D. Kyle, Claire E. Sexton, Bernd Feige, Annemarie Luik, Jacqueline Lane, Richa Saxena, Simon G. Anderson, David A. Bechtold, William Dixon, Max A. Little, et al. Sleep and cognitive performance: Cross-sectional associations from the UK Biobank. Sleep Medicine, 38:85--91, 2017.
      [74] E Ray Dorsey, A Atreja, M Frasier, E Hafen, JD Hixson, D Karlin, J Kvedar, M.A. Little, KD Mandl, WJ Marks Jr, et al. Bridging computer science and biomedicine. Digital Biomarkers, 1, 2017.
      [75] R Schneider, J Adams, M Elson, C Tarolli, S Sharma, A Glidden, T Felong, A Zhan, R Korn, S Goldenthal, et al. Feasibility of using a smartphone application for the objective evaluation of Parkinson's disease. In MOVEMENT DISORDERS, volume 32. WILEY, 2017.
      [76] AL Lima, LJW Evers, T Hahn, L Bataille, JL Hamilton, MA Little, BR Bloem, and MJ Faber. Gait, freezing of gait and falls detection using wearable sensors: A systematic review. In MOVEMENT DISORDERS, volume 32. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2017.
      [77] Florian Lipsmeier, Ignacio Fernandez Garcia, Detlef Wolf, Timothy Kilchenmann, Alf Scotland, Jens Schjodt-Eriksen, Wei-Yi Cheng, Juliane Siebourg-Polster, Liping Jin, Jay Soto, et al. Successful passive monitoring of early-stage Parkinson’s disease patient mobility in a Phase I RG7935/PRX002 clinical trial with smartphone sensors. Movement Disorders, 32:S358--359, 2017.
      [78] Ana LS de Lima, Tim Hahn, Luc JW Evers, Nienke M de Vries, Eli Cohen, Michal Afek, Lauren Bataille, Margaret Daeschler, Kasper Claes, Babak Boroojerdi, Dolors Terricabras, Max A. Little, Heribert Baldus, Bastiaan R Bloem, and Marjan J Faber. Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. PLoS One, 12(12):e0189161, 2017.
      [79] Andong Zhan, Max A Little, Denzil A Harris, Solomon O Abiola, E Dorsey, Suchi Saria, and Andreas Terzis. High frequency remote monitoring of Parkinson's disease via smartphone: Platform overview and medication response detection. arXiv preprint arXiv:1601.00960, 2016.
      [80] Jacqueline M Lane, Irma Vlasac, Simon G Anderson, Simon Kyle, William G Dixon, David A Bechtold, Shubhroz Gill, Max A Little, Annemarie Luik, Andrew Loudon, et al. Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UK Biobank. Nature Communications, 7:10889, 2016.
      [81] Alberto J Espay, Paolo Bonato, Fatta B Nahab, Walter Maetzler, John M Dean, Jochen Klucken, Bjoern M Eskofier, Aristide Merola, Fay Horak, Anthony E Lang, et al. Technology in Parkinson's disease: Challenges and opportunities. Movement Disorders, 31(9):1272--1282, 2016.
      [82] Ken Kubota, Jason Chen, and Max A. Little. Machine learning for large-scale wearable sensor data in Parkinson disease: concepts, promises, pitfalls and futures. Movement Disorders, 31(9):1314--1326, 2016.
      [83] Kelly L. Andrzejewski, Ariel V. Dowling, David Stamler, Timothy J. Felong, Denzil A. Harris, Cynthia Wong, Hang Cai, Ralf Reilmann, Max A. Little, Joseph T. Gwin, et al. Wearable Sensors in Huntington Disease: A Pilot Study. Journal of Huntington's Disease, 5(2):199--206, 2016.
      [84] Yordan P Raykov, Emre Ozer, Ganesh Dasika, Alexis Boukouvalas, and Max A Little. Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction. In UBICOMP'16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 1016--1027. ACM, 2016.
      [85] Yordan P. Raykov, Alexis Boukouvalas, Fahd Baig, and Max A. Little. What to do when K-means clustering fails: a simple yet principled alternative algorithm. PLoS One, 11(9):e0162259, 2016.
      [86] Yordan P. Raykov, Alexis Boukouvalas, and Max A. Little. Simple approximate MAP inference for Dirichlet processes. Electronic Journal of Statistics, 10(2):3548--3578, 2016.
      [87] Ana Lígia Silva de Lima, Tim Hahn, Nienke M de Vries, Eli Cohen, Lauren Bataille, Max A Little, Heribert Baldus, Bastiaan R Bloem, and Marjan J Faber. Large-scale wearable sensor deployment in Parkinson’s patients: the Parkinson@ Home Study Protocol. JMIR Research Protocols, 5(3):e172, 2016.
      [88] Siddharth Arora, Vinayak Venkataraman, Andong Zhan, S Donohue, Kevin M Biglan, E Ray Dorsey, and Max A Little. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study. Parkinsonism & related disorders, 21(6):650--653, 2015.
      [89] Diane Stephenson, Michele T Hu, Klaus Romero, Kieran Breen, David Burn, Yoav Ben-Shlomo, Atul Bhattaram, Maria Isaac, Charles Venuto, Ken Kubota, et al. Precompetitive Data Sharing as a Catalyst to Address Unmet Needs in Parkinson’s Disease. Journal of Parkinson's Disease, 5(3):581--594, 2015.
      [90] Harvey Rodda and Max A. Little. Understanding Mathematical and Statistical Techniques in Hydrology: An Examples-based Approach, 2015.
      [91] M.A. Little Y.P. Raykov, A. Boukouvalas. Iterative collapsed MAP inference for Bayesian nonparametrics. In NIPS 2015 Workshop: Bayesian Nonparametrics: The Next Generation, 2015.
      [92] M.A. Little Y.P. Raykov, A. Boukouvalas. MAP for Exponential Family Dirichlet Process Mixture Models. In NIPS 2015 Workshop: Nonparametric Methods for Large Scale Representation Learning, 2015.
      [93] DA Harris, Andong Zhan, SO Abiola, S Saria, MA Little, K Biglan, and ER Dorsey. Smartphone-PD: Preliminary results of an mHealth application to track and quantify characteristics of Parkinson disease in real-time. Movement Disorders, 30(10):e6--e7, 2015.
      [94] Karen Spencer, John Ainsworth, John Mcbeth, Max A Little, David Schultz, Caroline Sanders, and William Dixon. Using smartphones to examine the association between weather and joint pain in patients with rheumatoid arthritis: a feasibility study. In http://www. farrinstitute. org/events-courses/event/the-farr-institute-international-conference-2015, 2015.
      [95] Paul J Moore, Max A Little, Patrick E McSharry, Guy M Goodwin, and John R Geddes. Correlates of Depression in Bipolar Disorder. Proceedings of the Royal Society B: Biological Sciences, 281(1776):2013--2320, 2014.
      [96] Siddharth Arora, Vinayak Venkataraman, Sean Donohue, Kevin M Biglan, Earl R Dorsey, and Max A Little. High accuracy discrimination of Parkinson's disease participants from healthy controls using smartphones. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3641--3644. IEEE, 2014.
      [97] Athanasios Tsanas, Max A Little, Cynthia Fox, and Lorraine O Ramig. Objective Automatic Assessment of Rehabilitative Speech Treatment in Parkinson's Disease. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(1):181--190, 2014.
      [98] Athanasios Tsanas, Matías Zañartu, Max A Little, Cynthia Fox, Lorraine O Ramig, and Gari D Clifford. Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering. The Journal of the Acoustical Society of America, 135(5):2885--2901, 2014.
      [99] Paul J Moore, Max A Little, Patrick E McSharry, Guy M Goodwin, and John R Geddes. Mood dynamics in bipolar disorder. International journal of bipolar disorders, 2:1--9, 2014.
      [100] Max A Little. An efficient, approximate path-following algorithm for elastic net based nonlinear spike enhancement. In 2014 22nd European Signal Processing Conference (EUSIPCO), pages 1442--1446. IEEE, 2014.
      [101] PJ Moore and MA Little. Enhancements to a method of analogues forecasting algorithm. IEICE Proceedings Series, 46(B2L-B3), 2014.
      [102] Siddharth Arora, Max A Little, and Patrick E McSharry. Nonlinear and Nonparametric Modeling Approaches for Probabilistic Forecasting of the US Gross National Product. Studies in Nonlinear Dynamics and Econometrics, 17(4):395--420, 2013.
      [103] M.A. Little, P. Wicks, T.E. Vaughan, and A. Pentland. Quantifying short term dynamics of Parkinson’s disease using self-reported symptom data from an Internet social network. Journal of Medical Internet Research, 15(1):e20, 2013.
      [104] M.A. Little and N.S. Jones. Signal processing for molecular and cellular biological physics: an emerging field. Philosophical Transactions of the Royal Society A, 371(1984):20110546, 2013.
      [105] Patrick E McSharry, Max A Little, Harvey JE Rodda, and John Rodda. Quantifying flood risk of extreme events using density forecasts based on a new digital archive and weather ensemble predictions. Quarterly Journal of the Royal Meteorological Society, 139(671):328--333, 2013.
      [106] Ben D Fulcher, Max A Little, and Nick S Jones. Highly comparative time-series analysis: the empirical structure of time series and their methods. Journal of the Royal Society Interface, 10(83):20130048, 2013.
      [107] A Tsanas, MA Little, CM Fox, and LO Ramig. Automatic grouping of acceptable or unacceptable vocalizations in people with Parkinson's disease. Movement Disorders, 28:125, 2013.
      [108] Paul Wicks and Max Little. The virtuous circle of the quantified self: A human computational approach to improved health outcomes. In Handbook of human computation, pages 105--129. Springer New York New York, NY, 2013.
      [109] Athanasios Tsanas, Max A Little, Patrick E McSharry, and OCIAM Athanasios Tsanas. Statistical Analysis and Mapping of the Unified Parkinson’s Disease Rating Scale to Hoehn and Yahr Staging. Parkinsonism and Related Disorders, 18(5):697--699, 2012.
      [110] Athanasios Tsanas, M.A. Little, P McSharry, Jennifer Spielman, and L Ramig. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. IEEE Transactions on Biomedical Engineering, 59(5):1264--1271, 2012.
      [111] A Tsanas, MA Little, and PE McSharry. A methodology for the analysis of medical data. In Handbook of systems and complexity in health, pages 113--125. Springer New York New York, NY, 2012.
      [112] P Moore, M Little, P Mcsharry, JR Geddes, and G Goodwin. Erratum: Forecasting depression in bipolar disorder (IEEE Transactions on Biomedical Engineering (2012) 59: 10 (2801-2807)). IEEE Transactions on Biomedical Engineering, 59(12), 2012.
      [113] Athanasios Tsanas, Max A Little, Patrick E McSharry, and Lorraine O Ramig. Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity. Journal of the Royal Society Interface, 8(59):842--855, 2011.
      [114] Athanasios Tsanas, Max A Little, Patrick E McSharry, and Lorraine O Ramig. Robust Parsimonious Selection of Dysphonia Measures for Telemonitoring of Parkinson's Disease Symptom Severity. In Models And Analysis Of Vocal Emissions For Biomedical Applications: 7th International Workshop, page 169, 2011.
      [115] Max A Little, Bradley C Steel, Fan Bai, Yoshiyuki Sowa, Thomas Bilyard, David M Mueller, Richard M Berry, and Nick S Jones. Steps and Bumps: Precision Extraction of Discrete States of Molecular Machines. Biophysical Journal, 101(2):477--485, 2011.
      [116] Max A Little. Mathematical foundations of nonlinear, non-Gaussian, and time-varying digital speech signal processing. In Advances in Nonlinear Speech Processing: 5th International Conference on Nonlinear Speech Processing, NOLISP 2011, Las Palmas de Gran Canaria, Spain, November 7-9, 2011. Proceedings 5, pages 9--16. Springer Berlin Heidelberg, 2011.
      [117] Max A Little and Nick S Jones. Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, 467(2135), 2011.
      [118] Max A. Little and Nick S. Jones. Generalized methods and solvers for noise removal from piecewise constant signals. II. New methods. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 467(2135), 2011.
      [119] Athanasios Tsanas, Max A Little, Patrick E McSharry, and Lorraine O Ramig. Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests. IEEE Transactions on Biomedical Engineering, 57(4):884--893, 2010.
      [120] Athanasios Tsanas, Max A Little, Patrick E McSharry, and Lorraine O Ramig. Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson's disease progression. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 594--597. IEEE, 2010.
      [121] Max A Little and Nick S Jones. Sparse Bayesian step-filtering for high-throughput analysis of molecular machine dynamics. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 4162--4165. IEEE, 2010.
      [122] Max A Little and John P Bloomfield. Robust evidence for random fractal scaling of groundwater levels in unconfined aquifers. Journal of hydrology, 393(3-4):362--369, 2010.
      [123] Athanasios Tsanas, Max A Little, and Patrick E McSharry. A simple filter benchmark for feature selection, 2010.
      [124] A. Tsanas, M.A. Little, P.E. McSharry, and L.O. Ramig. New nonlinear markers and insights into speech signal degradation for effective tracking of Parkinson’s disease symptom severity. In International Symposium on Nonlinear Theory and its Applications (NOLTA), volume 64:8.1, pages 457--460, 2010.
      [125] Paul J Moore, Max A Little, Patrick E McSharry, John R Geddes, and Guy M Goodwin. Forecasting Depression in Bipolar Disorder using Cellphone Telemonitoring. innovations, 10:11, 2010.
      [126] Max A Little and Nick S Jones. Recovering Piecewise Constant Signals from Noisy Time Series. IEICE Proceedings Series, 44(C1L-A1), 2010.
      [127] John C Rodda, Max A Little, Harvey JE Rodda, and Patrick E McSharry. A comparative study of the magnitude, frequency and distribution of intense rainfall in the United Kingdom. International Journal of Climatology, 30(12):1776--1783, 2010.
      [128] Max A Little, Patrick E McSharry, Eric J Hunter, Jennifer Spielman, and Lorraine O Ramig. Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 56(4):1015--1022, 2009.
      [129] Harvey JE Rodda, Max A Little, Rose G Wood, Nina MacDougall, and Patrick E McSharry. A digital archive of extreme rainfalls in the British Isles from 1866 to 1968 based on British Rainfall. Weather, 64(3):71--75, 2009.
      [130] Max A Little, Patrick E McSharry, and James W Taylor. Generalised Linear Models for Site-Specific Density Forecasting of UK Daily Rainfall. Monthly Weather Review, 137:1031--1047, 2009.
      [131] John Rodda, Max Little, Harvey Rodda, and Patrick McSharry. A comparative study of the magnitude, frequency and distribution of intense rainfall in the United Kingdom. International Journal of Climatology, 30(12):1776--1783, 2009.
      [132] Max A. Little, Declan Costello, and Meredydd Harries. Objective Dysphonia Quantification in Vocal Fold Paralysis: Comparing Nonlinear With Classical Measures. Journal of Voice, 25(1):21--31, 2009.
      [133] MA Little, PE McSharry, and JW Taylor. Parsimonious modeling of uk daily rainfall for density forecasting. Geophysical Research Abstracts, EGU General Assembly, Vienna 2008, 10, 2008.
      [134] Max A Little, Harvey JE Rodda, and Patrick E McSharry. Bayesian objective classification of extreme UK daily rainfall for flood risk applications. Hydrology and Earth System Sciences Discussions, 5(6):3033--3060, 2008.
      [135] Max Andrew Little. Method of modifying low frequency components of a digital audio signal, June 19 2007. US Patent 7,233,833.
      [136] Max A. Little, Patrick McSharry, Stephen Roberts, Declan Costello, and Irene Moroz. Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMedical Engineering OnLine, 6:23, 2007.
      [137] Max A Little. Biomechanically informed nonlinear speech signal processing. PhD thesis, University of Oxford, 2007.
      [138] Max A. Little, Patrick McSharry, Irene Moroz, and Stephen Roberts. Nonlinear, biophysically-informed speech pathology detection. In Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, volume 2, pages II--II. IEEE, 2006.
      [139] Max A Little, Patrick E McSharry, Irene M Moroz, and Stephen J Roberts. Testing the assumptions of linear prediction analysis in normal vowels. The Journal of the Acoustical Society of America, 119(1):549--558, 2006.
      [140] Daniel Heesch and Max Little. Decision-making in variable environments—a case of group selection and inter-generational conflict? Theoretical population biology, 69(2):121--128, 2006.
      [141] Diego Perugini, Max A. Little, and Giampiero Poli. Time series to petrogenesis: analysis of oscillatory zoning patterns in plagioclase crystals from lava flows. Period Mineral, 75(2-3):263--276, 2006.
      [142] Alastair Sibbald and Max Andrew Little. Method of audio signal processing for a loudspeaker located close to an ear, May 18 2004. US Patent 6,738,479.
      [143] Max A. Little and Daniel Heesch. Chaotic root-finding for a small class of polynomials. Journal of Difference Equations and Applications, 10(11):949--953, 2004.
      [144] M Little, I Moroz, P McSharry, and S Roberts. Variational integration for speech signal processing. arXiv, 2004.

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