Adjunct Associate Professor Mark McDonnell
Grant Funded Researcher (C)
Australian Institute for Machine Learning - Projects
Faculty of Sciences, Engineering and Technology
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Appointments
Date Position Institution name 2023 - ongoing Grant-funded researcher in Continual Learning University of Adelaide 2018 - ongoing Machine Learning Specialist Consilium Technology 2017 - ongoing CTO Athlete's AI 2015 - 2021 Associate Professor University of South Australia 2012 - 2012 Visiting Professor University of British Columbia 2010 - 2014 Senior Research Fellow University of South Australia 2007 - 2023 Adjunct Associate Professor University of Adelaide 2007 - 2009 Research Fellow University of South Australia 2006 - 2007 Lecturer University of Adelaide -
Education
Date Institution name Country Title 2001 - 2006 University of Adelaide Australia PhD 1999 - 2000 University of Adelaide Australia First Class Honours in Applied Mathematics 1993 - 1997 University of Adelaide Australia Bachelor of Engineering (Electronic) -
Certifications
Date Title Institution name Country 2023 Machine Learning Specialty AWS -
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Journals
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Books
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Book Chapters
Year Citation 2014 McDonnell, M. D. (2014). Distributed bandpass filtering and signal demodulation in cortical network models. In Understanding Complex Systems (pp. 155-166). Springer International Publishing.
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Conference Papers
Year Citation 2023 McKenzie, M. C., & McDonnell, M. D. (2023). Hyperparameter Selection in Reinforcement Learning Using the “Design of Experiments” Method. In Procedia Computer Science Vol. 222 (pp. 35-44). Elsevier BV.
2023 McDonnell, M. D., Gong, D., Parvaneh, A., Abbasnejad, E., & Hengel, A. V. D. (2023). RanPAC: Random Projections and Pre-trained Models for Continual Learning.. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), NeurIPS. 2021 Shirazi, A. Z., McDonnell, M. D., Fornaciari, E., Bagherian, N. S., Scheer, K. G., Samuel, M. S., . . . Gomez, G. A. (2021). A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma. In CLINICAL CANCER RESEARCH Vol. 27 (pp. 2 pages). AMER ASSOC CANCER RESEARCH.
2020 McDonnell, M. D., Moezzi, B., & Brinkworth, R. S. A. (2020). Using Style-Transfer to Understand Material Classification for Robotic Sorting of Recycled Beverage Containers. In Proceedings of the Digital Image Computing: Techniques and Applications (DICTA 2019) (pp. 1-8). Online: IEEE.
Scopus12020 Chamchong, R., Gao, W., & McDonnell, M. D. (2020). Thai handwritten recognition on text block-based from thai archive manuscripts. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR (pp. 1346-1351). Online: IEEE.
Scopus82020 McDonnell, M. D., & Gao, W. (2020). Acoustic Scene Classification Using Deep Residual Networks with Late Fusion of Separated High and Low Frequency Paths. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings Vol. 2020-May (pp. 141-145). New York, NY, USA: IEEE.
Scopus63 WoS442020 Gao, W., Hashemi-Sakhtsari, A., & McDonnell, M. D. (2020). End-to-End Phoneme Recognition using Models from Semantic Image Segmentation. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 7 pages). New York, NY, USA: IEEE.
Scopus1 WoS12019 McDonnell, M. D., Mostafa, H., Wang, R., & Schaik, A. (2019). Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems. In Proceedings of the 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS 2019) (pp. 197-204). Online: IEEE.
Scopus2 WoS22019 McKenzie, M., & Mcdonnell, M. D. (2019). Degradation of Performance in Reinforcement Learning with State Measurement Uncertainty. In 2019 Military Communications and Information Systems Conference, MilCIS 2019 - Proceedings (pp. 1-5). Online: IEEE.
Scopus2 WoS42019 Madakkatel, I., Chiera, B., & McDonnell, M. D. (2019). Predicting Financial Well-Being Using Observable Features and Gradient Boosting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11919 LNAI (pp. 228-239). Online: Springer International Publishing.
2019 Stamatescu, V., & McDonnell, M. D. (2019). Diagnosing Convolutional Neural Networks using Their Spectral Response. In 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018 (pp. 8 pages). Online: IEEE.
Scopus1 WoS12018 McDonnell, M. D. (2018). Training wide residual networks for deployment using a single bit for each weight. In 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings (pp. 15 pages). Vancouver, BC, Canada.
Scopus212018 Bagchi, S., & McDonnell, M. D. (2018). A model of neurobiologically plausible least-squares learning in visual cortex. In 2018 International Joint Conference on Neural Networks Vol. 2018-July (pp. 1-8). New York, NY, USA: IEEE.
2018 Gunn, L., Smet, P., Arbon, E., & McDonnell, M. D. (2018). Anomaly Detection in Satellite Communications Systems using LSTM Networks. In 2018 Military Communications and Information Systems Conference, MilCIS 2018 - Proceedings (pp. 6 pages). Piscataway, New Jersey, USA: IEEE.
Scopus12 WoS72017 Yousefi-Azar, M., & McDonnell, M. D. (2017). Semi-supervised convolutional extreme learning machine. In Proceedings of the International Joint Conference on Neural Networks Vol. 2017-May (pp. 1968-1974). Anchorage, AK: IEEE.
Scopus10 WoS82017 De Chazal, P., & McDonnell, M. D. (2017). Regularized training of the extreme learning machine using the conjugate gradient method. In 2017 International Joint Conference on Neural Networks (IJCNN) Vol. 2017-May (pp. 1802-1808). Anchorage, AK: IEEE.
Scopus4 WoS42017 Gao, W., & McDonnell, M. D. (2017). Analysis of Gradient Degradation and Feature Map Quality in Deep All-Convolutional Neural Networks Compared to Deep Residual Networks. In D. Liu, S. Xie, Y. Li, D. Zhao, & E. S. M. ElAlfy (Eds.), Neural Information Processing Conference Proceedings (ICONIP 2017) Vol. 10635 (pp. 612-621). Guangzhou, PEOPLES R CHINA: SPRINGER INTERNATIONAL PUBLISHING AG.
Scopus1 WoS12017 Yousefi-Azar, M., Hamey, L., Varadharajan, V., & McDonnell, M. D. (2017). Fast, automatic and scalable learning to detect android malware. In D. Liu, S. Xie, Y. Li, D. Zhao, & E. S. M. ElAlfy (Eds.), Neural Information Processing (ICONIP) Vol. 10638 (pp. 848-857). Guangzhou, PEOPLES R CHINA: SPRINGER INTERNATIONAL PUBLISHING AG.
Scopus5 WoS52016 Stamatescu, V., Wong, S., McDonnell, M. D., & Kearney, D. (2016). Learned filters for object detection in multi-object visual tracking. In F. A. Sadjadi, & A. Mahalanobis (Eds.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 9844 (pp. 14 pages). Baltimore, MD: SPIE-INT SOC OPTICAL ENGINEERING.
Scopus3 WoS22016 Tissera, M. D., & McDonnell, M. D. (2016). Modular expansion of the hidden layer in Single Layer Feedforward neural Networks. In 2016 Intemational Joint Conference on Neural Networks (IJCNN) Vol. 2016-October (pp. 2939-2945). Vancouver, Canada: IEEE.
Scopus6 WoS52016 McDonnell, M., McKilliam, R., & De Chazal, P. (2016). On the importance of pair-wise feature correlations for image classification. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2016) Vol. 2016-October (pp. 2290-2297). http://www.ijcnn.org: IEEE.
Scopus3 WoS32016 Padilla, D. E., & McDonnell, M. D. (2016). Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning. In 2016 Intemational Joint Conference on Neural Networks (IJCNN) Vol. 2016-October (pp. 1187-1194). Vancouver, Canada: IEEE.
2016 De Chazal, P., & McDonnell, M. D. (2016). Efficient computation of the Levenberg-Marquardt algorithm for feedforward networks with linear outputs. In 2016 Intemational Joint Conference on Neural Networks (IJCNN) Vol. 2016-October (pp. 68-75). Vancouver, Canada: IEEE.
Scopus2 WoS12016 Tissera, M. D., & McDonnell, M. D. (2016). Enhancing deep extreme learning machines by error backpropagation. In Proceedings of the International Joint Conference on Neural Networks Vol. 2016-October (pp. 735-739). Vancouver, CANADA: IEEE.
Scopus3 WoS42016 Wong, S. C., Gatt, A., Stamatescu, V., & McDonnell, M. D. (2016). Understanding Data Augmentation for Classification: When to Warp?. In A. W. C. Liew, B. Lovell, C. Fookes, J. Zhou, Y. Gao, M. Blumenstein, & Z. Wang (Eds.), 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016 (pp. 59-64). Gold Coast, AUSTRALIA: IEEE.
Scopus687 WoS1052015 Gao, X., Grayden, D., & McDonnell, M. (2015). Modeling electrode place discrimination in cochlear implants: Analysis of the influence of electrode array insertion depth. In International IEEE/EMBS Conference on Neural Engineering, NER Vol. 2015-July (pp. 691-694). Montpellier, FRANCE: IEEE.
Scopus2 WoS22015 McDonnell, M., & Vladusich, T. (2015). Enhanced image classification with a fast-learning shallow convolutional neural network. In Proceedings of the International Joint Conference on Neural Networks Vol. 2015-September (pp. 1-7). online: IEEE.
Scopus81 WoS242014 Gao, X., Graydeny, D., & McDonnell, M. (2014). Inferring the dynamic range of electrode current by using an information theoretic model of cochlear implant stimulation. In Proceedings of the IEEE Information Theory Workshop (ITW 2014) (pp. 346-350). Piscataway, NJ: IEEE.
Scopus3 WoS22014 Gao, X., Grayden, D., & McDonnell, M. (2014). Using convex optimization to compute channel capacity in a channel model of cochlear implant stimulation. In IEEE International Symposium on Information Theory - Proceedings (pp. 2919-2923). Honolulu, HI: IEEE.
Scopus2 WoS22014 Wang, S., Guo, W., & McDonnell, M. (2014). Distance distributions for real cellular networks. In Proceedings - IEEE INFOCOM (pp. 181-182). Toronto, CANADA: IEEE.
Scopus6 WoS32014 Wang, S., Guo, W., & McDonnell, M. (2014). Transmit pulse shaping for molecular communications. In Proceedings - IEEE INFOCOM (pp. 209-210). Toronto, CANADA: IEEE.
Scopus21 WoS152014 Wang, S., Guo, W., Qiu, S., & McDonnell, M. (2014). Performance of macro-scale molecular communications with sensor cleanse time. In 2014 21st International Conference on Telecommunications, ICT 2014 (pp. 363-368). Lisbon, PORTUGAL: IEEE.
Scopus13 WoS112014 Wang, S., Guo, W., & McDonnell, M. (2014). Downlink interference estimation without feedback for heterogeneous network interference avoidance. In 2014 21st International Conference on Telecommunications, ICT 2014 (pp. 82-87). Lisbon, PORTUGAL: IEEE.
Scopus8 WoS52014 Padilla, D., & McDonnell, M. (2014). A neurobiologically plausible vector symbolic architecture. In Proceedings of the 8th IEEE International Conference on Semantic Computing (ICSC 2014) (pp. 242-245). online: IEEE.
Scopus4 WoS22014 Tissera, M., & McDonnell, M. (2014). Enabling 'question answering' in the MBAT vector symbolic architecture by exploiting orthogonal random matrices. In Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014 (pp. 171-174). Online: IEEE.
Scopus2 WoS22013 Padilla, D., Brinkworth, R., & McDonnell, M. (2013). Performance of a hierarchical temporal memory network in noisy sequence learning. In W. Wahab, & A. Muis (Eds.), Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics (pp. 45-51). Yogyakarta, INDONESIA: IEEE.
Scopus15 WoS122013 McDonnell, M., & Ward, L. (2013). Identifying positive roles for endogenous stochastic noise during computation in neural systems. In Proceedings, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013) Vol. 2013 (pp. 5232-5235). Osaka, Japan: IEEE.
Scopus1 WoS1 Europe PMC12013 Gao, X., Grayden, D., & McDonnell, M. (2013). Information theoretic optimization of cochlear implant electrode usage probabilities. In Proceedings, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013) Vol. 2013 (pp. 5974-5977). Osaka, Japan: IEEE.
Scopus7 WoS7 Europe PMC22012 Moroz, A., McDonnell, M., Burkitt, A., Grayden, D., & Meffin, H. (2012). Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Vol. 2012 (pp. 2965-2968). San Diego, California: IEEE.
Scopus6 WoS6 Europe PMC22011 Prettejohn, B., & McDonnell, M. (2011). Effect of network topology in opinion formation models. In C. Guttmann, F. Dignum, & M. Georgeff (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 6066 LNAI (pp. 114-124). York Univ, Toronto, CANADA: SPRINGER-VERLAG BERLIN.
Scopus3 WoS32009 McDonnell, M. D. (2009). Applying stochastic signal quantization theory to the robust digitization of noisy analog signals. In V. In, P. Longhini, & A. Palacious (Eds.), Understanding Complex Systems Vol. 2009 (pp. 249-261). Poipu Beach, HI: SPRINGER-VERLAG BERLIN.
Scopus12009 Li, F., McDonnell, M. D., Amblard, P. O., & Grant, A. J. (2009). Sensor selection for distributed detection via multiaccess channels. In Proceedings of the 2009 Australian Communications Theory Workshop, AusCTW 2009 (pp. 77-82). Univ New S Wales, Sydney, AUSTRALIA: IEEE.
Scopus12009 Stocks, N. G., Nikitin, A. P., McDonnell, M. D., & Morse, R. P. (2009). The role of stochasticity in an information-optimal neural population code. In M. Inoue, S. Ishii, Y. Kabashima, & M. Okada (Eds.), Journal of Physics: Conference Series Vol. 197 (pp. 11 pages). Kyoto, JAPAN: IOP PUBLISHING LTD.
2008 McDonnell, M. D., Amblard, P. O., & Stocks, N. G. (2008). Stochastic Pooling Networks: a biologically inspired model for robust signal detection and compression. In D. Kearney, V. Nguyen, G. Gioiosa, & T. Hendtlass (Eds.), 2008 Third International Conference on Bio-Inspired Computing: Theories and Applications (pp. 75-81). Adelaide, South Australia: IEEE.
Scopus1 WoS12008 McDonnell, M. D. (2008). Reliable communication and sensing via parallel redundancy in noisy digital receivers. In Australian Communications Theory Workshop, 2008, AusCTW 2008 (pp. 23-28). Christchurch, New Zealand: IEEE.
Scopus5 WoS42008 McDonnell, M. D. (2008). Signal compression in biological sensory systems: information theoretic performance limits. In D. V. Nicolau, D. Abbott, K. KalantarZadeh, T. DiMatteo, & S. M. Bezrukov (Eds.), BioMEMS and Nanotechnology III Vol. 6799 (pp. 679913-1-679913-10). Canberra, ACT: SPIE.
Scopus12007 Amblard, P. O., Zozor, S., McDonnell, M. D., & Stocks, N. G. (2007). Pooling networks for a discrimination task: Noise-enhanced detection. In S. M. Bezrukov (Ed.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 6602 (pp. 12 pages). Florence, ITALY: SPIE-INT SOC OPTICAL ENGINEERING.
Scopus11 WoS22007 Martorell, F., McDonnell, M., Abbott, D., & Rubio, A. (2007). SNDR enhancement in noisy sinusoidal signals by non-linear processing elements. In M. Macucci, L. K. J. Vandamme, C. Ciofi, & M. B. Weissman (Eds.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 6600 (pp. 1-100). USA: International Society for Optical Engineering.
2007 McDonnell, M. (2007). Signal estimation via averaging of coarsely quantised signals. In P. Mareels (Ed.), Proceedings of 2007 Information, Decision and Control, IDC (pp. 100-105). Australia: IEEE.
Scopus32007 McDonnell, M., Stocks, N., & Abbott, D. (2007). Optimal coding of a random stimulus by a population of parallel neuron models. In S. M. Bezrukov (Ed.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 6602 (pp. 1-100). USA: International Society for Optical Engineering.
2006 McDonnell, M., & Abbott, D. (2006). A biologically inspired model for signal compression. In D. V. Nicolau (Ed.), Proceedings of Smart Materials, Nano-, and Micro-Smart Systems 2006 Vol. 6416 (pp. 1-12). USA: SPIE.
Scopus1 WoS12006 McDonnell, M., Amblard, P. O., Stocks, N., Zozor, S., & Abbott, D. (2006). High-resolution optimal quantization for stochastic pooling networks. In Axel Bender (Ed.), Proceedings of Smart Materials, Nano-, and Micro-Smart Systems 2006 Vol. 6417 (pp. CDROM1-CDROM15). USA: SPIE.
Scopus3 WoS12006 McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2006). How to use noise to reduce complexity in quantization. In A. Bender (Ed.), Proceedings of Microelectronics, MEMS, and Nanotechnology 2005 Vol. 6039 (pp. 60390E-1-60390E-12). http://www.spie.org/conferences/programs/05/au/: SPIE.
Scopus12005 McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2005). Optimal quantization and suprathreshold stochastic resonance. In N. G. Stocks, D. Abbott, & R. P. Morse (Eds.), Fluctuations and noise in biological, biophysical, and biomedical systems III : 24-26 May, 2005, Austin, Texas, USA Vol. 5841 (pp. 164-173). Austin, Texas, USA: SPIE.
Scopus1 WoS12005 McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2005). Analog to digital conversion using suprathreshold stochastic resonance. In S. Al Sarawi (Ed.), Proceedings of the SPIE International Symposium on Smart Structures, Devices, and Systems II Vol. 5649 (pp. 75-84). Bellingham, Washington, USA: SPIE.
Scopus13 WoS72005 Martorell, F., McDonnell, M., Rubio, A., & Abbott, D. (2005). Using noise to break the noise barrier in circuits. In S. Al Sarawi (Ed.), Proceedings of the SPIE International Symposium on Smart Structures, Devices, and Systems II Vol. 5649 (pp. 53-66). Bellingham, Washington, USA: SPIE.
Scopus7 WoS52004 McDonnell, M., & Abbott, D. (2004). Optimal quantization in neural coding. In F. Kschischang, & D. Tse (Eds.), Proceedings of the 2004 IEEE International Symposium on Information Theory (pp. 1-6). New Jersey, USA: IEEE.
Scopus1 WoS12004 McDonnell, M., & Abbott, D. (2004). Signal reconstruction via noise through a system of parallel threshold nonlinearities. In D. O'Shaughnessy (Ed.), Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 2 (pp. CD-ROM II - 809-CD-ROM II - 812). CD-ROM: IEEE.
Scopus9 WoS42004 McDonnell, M., Sethuraman, S., Kish, L., & Abbott, D. (2004). Cross-spectral measurement of neural signal transfer. In L. B. Kish (Ed.), Proceedings of SPIE on CD-ROM: Fluctuations and Noise 2004 Vol. 5471 (pp. CD-ROM 550-CD-ROM 559). CD-ROM: SPIE.
Scopus1 WoS12004 McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2004). Optimal quantization for energy-efficient information transfer in a population of neuron-like devices. In L. Kish (Ed.), Proceedings of SPIE on CD-ROM: Fluctuations and Noise 2004 Vol. 5471 (pp. CD-ROM 222-CD-ROM 232). CD-ROM: SPIE.
Scopus1 WoS12004 McDonnell, M., Abbott, D., & Pearce, C. (2004). Neural mechanisms for analog to digital conversion. In L. Faraone, & V. K. Varadan (Eds.), Proceedings of SPIE on CD-ROM: Microelectronics MEMS, and Nanotechnology 2003 Vol. 5275 (pp. CD-ROM 278-CD-ROM 286). CD-ROM: SPIE.
Scopus6 WoS62003 McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2003). The data processing inequality and stochastic resonance. In H. M. Jaenisch, & J. W. Handley (Eds.), Proceedings of SPIE Vol. 5114 Vol. 5114 (pp. 249-260). Washington, USA: SPIE.
Scopus2 WoS22003 McDonnell, M., & Abbott, D. (2003). Open questions for suprathreshold stochastic resonance in sensory neural models for motion detection using artificial insect vision. In S. M. Bezrukov (Ed.), Proceedings of UPon 2003: Third International Conference on Unsolved Problems of Noise and Fluctuations in Physics, Biology, and High Technology Vol. 665 (pp. 51-58). Melville, New York, USA: American Institute of Physics.
Scopus2 WoS12002 McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2002). Maximising information transfer through nonlinear noisy devices. In D. V. Nicolau, & A. P. Lee (Eds.), Proceedings of SPIE Vol. 4937 Vol. 4937 (pp. 254-263). CDROM: SPIE.
Scopus2 WoS22001 McDonnell, M., Pearce, C., & Abbott, D. (2001). Neural information transfer in a noisy environment. In N. W. Bergmann (Ed.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 4591 (pp. 59-69). PO BOX 10 BELLINGHAM WASHINGTON USA: THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS.
Scopus2 WoS1 -
Preprint
Year Citation 2024 McDonnell, M. D., Gong, D., Abbasnejad, E., & Hengel, A. V. D. (2024). Premonition: Using Generative Models to Preempt Future Data Changes in
Continual Learning.2023 McDonnell, M. D., Gong, D., Parvaneh, A., Abbasnejad, E., & Hengel, A. V. D. (2023). RanPAC: Random Projections and Pre-trained Models for Continual Learning..
- Australian Research Council, ITTC, 2018-2022
- Australian Research Council, Discovery Project, 2017-2019
- Defence Science and Technology Organisation, 2004-2019
- National Health and Medical Research Council, 2012-2014
- Australian Research Council, Discovery Project, 2010-2014
- Australian Research Council, Discovery Project, 2007-2009
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