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1. Co-Clustering Multi-View Data Using the Latent Block Model

Author

Tobin, Joshua, Black, Michaela, Ng, James, Rankin, Debbie, Wallace, Jonathan, Hughes, Catherine, Hoey, Leane, Moore, Adrian, Wang, Jinling, Horigan, Geraldine, Carlin, Paul, McNulty, Helene, Molloy, Anne M, and Zhang, Mimi

Subjects
Statistics - Methodology
Abstract

The Latent Block Model (LBM) is a prominent model-based co-clustering method, returning parametric representations of each block cluster and allowing the use of well-grounded model selection methods. The LBM, while adapted in literature to handle different feature types, cannot be applied to datasets consisting of multiple disjoint sets of features, termed views, for a common set of observations. In this work, we introduce the multi-view LBM, extending the LBM method to multi-view data, where each view marginally follows an LBM. In the case of two views, the dependence between them is captured by a cluster membership matrix, and we aim to learn the structure of this matrix. We develop a likelihood-based approach in which parameter estimation uses a stochastic EM algorithm integrating a Gibbs sampler, and an ICL criterion is derived to determine the number of row and column clusters in each view. To motivate the application of multi-view methods, we extend recent work developing hypothesis tests for the null hypothesis that clusters of observations in each view are independent of each other. The testing procedure is integrated into the model estimation strategy. Furthermore, we introduce a penalty scheme to generate sparse row clusterings. We verify the performance of the developed algorithm using synthetic datasets, and provide guidance for optimal parameter selection. Finally, the multi-view co-clustering method is applied to a complex genomics dataset, and is shown to provide new insights for high-dimension multi-view problems.

Published
2024

2. Uveal melanoma immunogenomics predict immunotherapy resistance and susceptibility

Author

Leonard-Murali, Shravan, Bhaskarla, Chetana, Yadav, Ghanshyam S., Maurya, Sudeep K., Galiveti, Chenna R., Tobin, Joshua A., Kann, Rachel J., Ashwat, Eishan, Murphy, Patrick S., Chakka, Anish B., Soman, Vishal, Cantalupo, Paul G., Zhuo, Xinming, Vyas, Gopi, Kozak, Dara L., Kelly, Lindsey M., Smith, Ed, Chandran, Uma R., Hsu, Yen-Michael S., and Kammula, Udai S.

Published
2024
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3. 2024 American Heart Association and American Academy of Pediatrics Focused Update on Special Circumstances: Resuscitation Following Drowning: An Update to the American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care

Author

Dezfulian, Cameron, McCallin, Tracy E., Bierens, Joost, Dunne, Cody L., Idris, Ahamed H., Kiragu, Andrew, Mahgoub, Melissa, Shenoi, Rohit P., Szpilman, David, Terry, Mark, Tijssen, Janice A., Tobin, Joshua M., and Topjian, Alexis A.

Published
2024
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4. Adjuvant rituximab and elevated intratumoural CD8 expression are associated with sustained disease control after radiotherapy in a randomised trial of systemic therapy in early-stage follicular lymphoma

Author

MacManus, Michael P., Seymour, John F., Tsang, Hennes, Fisher, Richard, Keane, Colm, Sabdia, Muhammed B., Law, Soi C., Gunawardana, Jay, Nath, Karthik, Kazakoff, Stephen H., Marques-Piubelli, Mario L., Duenas, Daniela E., Green, Michael R., Roos, Daniel, O'Brien, Peter, McCann, Andrew, Tsang, Richard, Davis, Sidney, Christie, David, Cheah, Chan, Amanuel, Benhur, Cochrane, Tara, Butler, Jason, Johnston, Anna, Shanavas, Mohamed, Li, Li, Vajdic, Claire, Kridel, Robert, Shelton, Victoria, Hershenfield, Samantha, Baetz, Tara, Lebrun, David, Johnson, Nathalie, Brodtkorb, Marianne, Ludvigsen, Maja, d’Amore, Francesco, Thompson, Ella R., Blombery, Piers, Gandhi, Maher K., and Tobin, Joshua W.D.

Published
2024
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5. Outcomes for high-risk defining events in follicular lymphoma following frontline immunochemotherapy

Author

Tobin, Joshua W. D., Chikatamarla, Venkata A., Matic, Marko, Griffin, Alison, Chowdhury, Rakin, Salvaris, Ross, Goh, Amanda, Black, Harrison, Tong, Tsz Hung, Birks, Callum, Jain, Sanjiv, Goodall, Elizabeth, Sirdesai, Shreerang, Trevis, Thomas, Steinepreis, Elizabeth, Chen, Yiyang, Li, Li, Broadby, Glenn, Gutta, Naadir, Morris, Kirk, Cochrane, Tara, Trotman, Judith, Talaulikar, Dipti, Shortt, Jake, Hodges, Georgina, Hawkes, Eliza A., Cheah, Chan Y., Barraclough, Allison, Manos, Kate, Johnston, Anna, Royle, Jane, Mondello, Patrizia, Ansell, Stephen M., and Hapgood, Greg

Published
2024
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8. Clustering of Big Data with Mixed Features

Author

Tobin, Joshua and Zhang, Mimi

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Clustering large, mixed data is a central problem in data mining. Many approaches adopt the idea of k-means, and hence are sensitive to initialisation, detect only spherical clusters, and require a priori the unknown number of clusters. We here develop a new clustering algorithm for large data of mixed type, aiming at improving the applicability and efficiency of the peak-finding technique. The improvements are threefold: (1) the new algorithm is applicable to mixed data; (2) the algorithm is capable of detecting outliers and clusters of relatively lower density values; (3) the algorithm is competent at deciding the correct number of clusters. The computational complexity of the algorithm is greatly reduced by applying a fast k-nearest neighbors method and by scaling down to component sets. We present experimental results to verify that our algorithm works well in practice. Keywords: Clustering; Big Data; Mixed Attribute; Density Peaks; Nearest-Neighbor Graph; Conductance., Comment: 22 pages, 9 figures, for associated Python library, see https://pypi.org/project/CPFcluster/ , submitted to SDM 2021

Published
2020

9. 2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

Author

Barcala-Furelos, Roberto, Beerman, Stephen B., Bruckner, Marlies, Castrén, Maaret, Chong, ShuLing, Claesson, Andreas, Dunne, Cody L., Finan, Emer, Fukuda, Tatsuma, Lalgudi Ganesan, Saptharishi, Gately, Callum, Gois, Aecio, Gray, Seth, Halamek, Louis P., Hoover, Amber V., Hurst, Cameron, Josephsen, Justin, Kollander, Louise, Omar Kamlin, C., Kool, Mirjam, Li, Lei, Mecrow, Thomas S., Montgomery, William, Ristau, Patrick, Jayashree, Muralidharan, Schmidt, Andrew, Scquizzato, Tommaso, Seesink, Jeroen, Sempsrott, Justin, Lee Solevåg, Anne, Strand, Marya L., Szpilman, David, Szyld, Edgardo, Thom, Ogilvie, Tobin, Joshua M., Trang, Jacinta, Webber, Jonathon, Webster, Hannah K., Wellsford, Michelle, Berg, Katherine M., Bray, Janet E., Ng, Kee-Chong, Liley, Helen G., Greif, Robert, Carlson, Jestin N., Morley, Peter T., Drennan, Ian R., Smyth, Michael, Scholefield, Barnaby R., Weiner, Gary M., Cheng, Adam, Djärv, Therese, Abelairas-Gómez, Cristian, Acworth, Jason, Andersen, Lars W., Atkins, Dianne L., Berry, David C., Bhanji, Farhan, Bierens, Joost, Bittencourt Couto, Thomaz, Borra, Vere, Böttiger, Bernd W., Bradley, Richard N., Breckwoldt, Jan, Cassan, Pascal, Chang, Wei-Tien, Charlton, Nathan P., Chung, Sung Phil, Considine, Julie, Costa-Nobre, Daniela T., Couper, Keith, Dainty, Katie N., Dassanayake, Vihara, Davis, Peter G., Dawson, Jennifer A., Fernanda de Almeida, Maria, De Caen, Allan R., Deakin, Charles D., Dicker, Bridget, Douma, Matthew J., Eastwood, Kathryn, El-Naggar, Walid, Fabres, Jorge G., Fawke, Joe, Fijacko, Nino, Finn, Judith C., Flores, Gustavo E., Foglia, Elizabeth E., Folke, Fredrik, Gilfoyle, Elaine, Goolsby, Craig A., Granfeldt, Asger, Guerguerian, Anne-Marie, Guinsburg, Ruth, Hatanaka, Tetsuo, Hirsch, Karen G., Holmberg, Mathias J., Hosono, Shigeharu, Hsieh, Ming-Ju, Hsu, Cindy H., Ikeyama, Takanari, Isayama, Tetsuya, Johnson, Nicholas J., Kapadia, Vishal S., Daripa Kawakami, Mandira, Kim, Han-Suk, Kleinman, Monica E., Kloeck, David A., Kudenchuk, Peter, Kule, Amy, Kurosawa, Hiroshi, Lagina, Anthony T., Lauridsen, Kasper G., Lavonas, Eric J., Lee, Henry C., Lin, Yiqun, Lockey, Andrew S., Macneil, Finlay, Maconochie, Ian K., John Madar, R., Malta Hansen, Carolina, Masterson, Siobhan, Matsuyama, Tasuku, McKinlay, Christopher J.D., Meyran, Daniel, Monnelly, Vix, Nadkarni, Vinay, Nakwa, Firdose L., Nation, Kevin J., Nehme, Ziad, Nemeth, Michael, Neumar, Robert W., Nicholson, Tonia, Nikolaou, Nikolaos, Nishiyama, Chika, Norii, Tatsuya, Nuthall, Gabrielle A., Ohshimo, Shinchiro, Olasveengen, Theresa M., Gene Ong, Yong-Kwang, Orkin, Aaron M., Parr, Michael J., Patocka, Catherine, Perkins, Gavin D., Perlman, Jeffrey M., Rabi, Yacov, Raitt, James, Ramachandran, Shalini, Ramaswamy, Viraraghavan V., Raymond, Tia T., Reis, Amelia G., Reynolds, Joshua C., Ristagno, Giuseppe, Rodriguez-Nunez, Antonio, Roehr, Charles C., Rüdiger, Mario, Sakamoto, Tetsuya, Sandroni, Claudio, Sawyer, Taylor L., Schexnayder, Steve M., Schmölzer, Georg M., Schnaubelt, Sebastian, Semeraro, Federico, Singletary, Eunice M., Skrifvars, Markus B., Smith, Christopher M., Soar, Jasmeet, Stassen, Willem, Sugiura, Takahiro, Tijssen, Janice A., Topjian, Alexis A., Trevisanuto, Daniele, Vaillancourt, Christian, Wyckoff, Myra H., Wyllie, Jonathan P., Yang, Chih-Wei, Yeung, Joyce, Zelop, Carolyn M., Zideman, David A., and Nolan, Jerry P.

Published
2024
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10. A systematic review of interventions for resuscitation following drowning

Author

Bierens, Joost, Bray, Janet, Abelairas-Gomez, Cristian, Barcala-Furelos, Roberto, Beerman, Stephen, Claesson, Andreas, Dunne, Cody, Fukuda, Tatsuma, Jayashree, Muralidharan, T Lagina, Anthony, Li, Lei, Mecrow, Tom, Morgan, Patrick, Schmidt, Andrew, Seesink, Jeroen, Sempsrott, Justin, Szpilman, David, Thom, Ogilvie, Tobin, Joshua, Webber, Jonathon, Johnson, Samantha, and Perkins, Gavin D

Published
2023
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13. 2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

Author

Berg, Katherine M., Bray, Janet E., Ng, Kee-Chong, Liley, Helen G., Greif, Robert, Carlson, Jestin N., Morley, Peter T., Drennan, Ian R., Smyth, Michael, Scholefield, Barnaby R., Weiner, Gary M., Cheng, Adam, Djärv, Therese, Abelairas-Gómez, Cristian, Acworth, Jason, Andersen, Lars W., Atkins, Dianne L., Berry, David C., Bhanji, Farhan, Bierens, Joost, Bittencourt Couto, Thomaz, Borra, Vere, Böttiger, Bernd W., Bradley, Richard N., Breckwoldt, Jan, Cassan, Pascal, Chang, Wei-Tien, Charlton, Nathan P., Chung, Sung Phil, Considine, Julie, Costa-Nobre, Daniela T., Couper, Keith, Dainty, Katie N., Dassanayake, Vihara, Davis, Peter G., Dawson, Jennifer A., de Almeida, Maria Fernanda, De Caen, Allan R., Deakin, Charles D., Dicker, Bridget, Douma, Matthew J., Eastwood, Kathryn, El-Naggar, Walid, Fabres, Jorge G., Fawke, Joe, Fijacko, Nino, Finn, Judith C., Flores, Gustavo E., Foglia, Elizabeth E., Folke, Fredrik, Gilfoyle, Elaine, Goolsby, Craig A., Granfeldt, Asger, Guerguerian, Anne-Marie, Guinsburg, Ruth, Hatanaka, Tetsuo, Hirsch, Karen G., Holmberg, Mathias J., Hosono, Shigeharu, Hsieh, Ming-Ju, Hsu, Cindy H., Ikeyama, Takanari, Isayama, Tetsuya, Johnson, Nicholas J., Kapadia, Vishal S., Kawakami, Mandira Daripa, Kim, Han-Suk, Kleinman, Monica E., Kloeck, David A., Ko, Ying-Chih, Kudenchuk, Peter, Kule, Amy, Kurosawa, Hiroshi, Lagina, Anthony T., Lauridsen, Kasper G., Lavonas, Eric J., Lee, Henry C., Lin, Yiqun, Lockey, Andrew S., Macneil, Finlay, Maconochie, Ian K., Madar, R. John, Malta Hansen, Carolina, Masterson, Siobhan, Matsuyama, Tasuku, McKinlay, Christopher J.D., Meyran, Daniel, Monnelly, Vix, Morrison, Laurie J., Nadkarni, Vinay, Nakwa, Firdose L., Nation, Kevin J., Nehme, Ziad, Nemeth, Michael, Neumar, Robert W., Nicholson, Tonia, Nikolaou, Nikolaos, Nishiyama, Chika, Norii, Tatsuya, Nuthall, Gabrielle A., Ohshimo, Shinchiro, Olasveengen, Theresa M., Ong, Yong-Kwang Gene, Orkin, Aaron M., Parr, Michael J., Patocka, Catherine, Perkins, Gavin D., Perlman, Jeffrey M., Rabi, Yacov, Raitt, James, Ramachandran, Shalini, Ramaswamy, Viraraghavan V., Raymond, Tia T., Reis, Amelia G., Reynolds, Joshua C., Ristagno, Giuseppe, Rodriguez-Nunez, Antonio, Roehr, Charles C., Rüdiger, Mario, Sakamoto, Tetsuya, Sandroni, Claudio, Sawyer, Taylor L., Schexnayder, Steve M., Schmölzer, Georg M., Schnaubelt, Sebastian, Semeraro, Federico, Singletary, Eunice M., Skrifvars, Markus B., Smith, Christopher M., Soar, Jasmeet, Stassen, Willem, Sugiura, Takahiro, Tijssen, Janice A., Topjian, Alexis A., Trevisanuto, Daniele, Vaillancourt, Christian, Wyckoff, Myra H., Wyllie, Jonathan P., Yang, Chih-Wei, Yeung, Joyce, Zelop, Carolyn M., Zideman, David A., Nolan, Jerry P., Barcala-Furelos, Roberto, Beerman, Stephen B., Castrén, Maaret, Chong, ShuLing, Claesson, Andreas, Dunne, Cody L., Ersdal, Hege L., Finan, Emer, Fuerch, Janene, Fukuda, Tatsuma, Ganesan, Saptharishi Lalgudi, Gately, Callum, Gray, Seth, Halamek, Louis P., Hoover, Amber V., Kollander, Louise, Kamlin, C. Omar, Koo, Mirjam, Li, Lei, Leone, Tina A., Mecrow, s, Montgomery, William, Ristau, Patrick, Jayashree, Muralidharan, Quek, Bin Huey, Schmidt, Andrew, Scquizzato, Tommaso, Seesink, Jeroen, Sempsrott, Justin, Shah, Birju A., Strand, Marya L., Szpilman, David, Szyld, Edgardo, Thio, Marta, Thom, Ogilvie, Tobin, Joshua M., Udaeta, Enrique, Webber, Jonathon, Webster, Hannah K., Wellsford, Michelle, and Yamada, Nicole K.

Published
2023
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15. Domain Randomization and Generative Models for Robotic Grasping

Author

Tobin, Joshua, Biewald, Lukas, Duan, Rocky, Andrychowicz, Marcin, Handa, Ankur, Kumar, Vikash, McGrew, Bob, Schneider, Jonas, Welinder, Peter, Zaremba, Wojciech, and Abbeel, Pieter

Subjects
Computer Science - Robotics, Computer Science - Learning
Abstract

Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often trained on as few as hundreds or thousands of unique object instances, and as a result generalization can be a challenge. In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis. We generate millions of unique, unrealistic procedurally generated objects, and train a deep neural network to perform grasp planning on these objects. Since the distribution of successful grasps for a given object can be highly multimodal, we propose an autoregressive grasp planning model that maps sensor inputs of a scene to a probability distribution over possible grasps. This model allows us to sample grasps efficiently at test time (or avoid sampling entirely). We evaluate our model architecture and data generation pipeline in simulation and the real world. We find we can achieve a $>$90% success rate on previously unseen realistic objects at test time in simulation despite having only been trained on random objects. We also demonstrate an 80% success rate on real-world grasp attempts despite having only been trained on random simulated objects., Comment: 8 pages, 11 figures. Submitted to 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)

Published
2017

16. Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model

Author

Christiano, Paul, Shah, Zain, Mordatch, Igor, Schneider, Jonas, Blackwell, Trevor, Tobin, Joshua, Abbeel, Pieter, and Zaremba, Wojciech

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Learning, Computer Science - Systems and Control
Abstract

Developing control policies in simulation is often more practical and safer than directly running experiments in the real world. This applies to policies obtained from planning and optimization, and even more so to policies obtained from reinforcement learning, which is often very data demanding. However, a policy that succeeds in simulation often doesn't work when deployed on a real robot. Nevertheless, often the overall gist of what the policy does in simulation remains valid in the real world. In this paper we investigate such settings, where the sequence of states traversed in simulation remains reasonable for the real world, even if the details of the controls are not, as could be the case when the key differences lie in detailed friction, contact, mass and geometry properties. During execution, at each time step our approach computes what the simulation-based control policy would do, but then, rather than executing these controls on the real robot, our approach computes what the simulation expects the resulting next state(s) will be, and then relies on a learned deep inverse dynamics model to decide which real-world action is most suitable to achieve those next states. Deep models are only as good as their training data, and we also propose an approach for data collection to (incrementally) learn the deep inverse dynamics model. Our experiments shows our approach compares favorably with various baselines that have been developed for dealing with simulation to real world model discrepancy, including output error control and Gaussian dynamics adaptation.

Published
2016

21. 2024 American Heart Association and American Academy of Pediatrics Focused Update on Special Circumstances: Resuscitation Following Drowning: An Update to the American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care.

Author

McCallin, Tracy E., Dezfulian, Cameron, Bierens, Joost, Dunne, Cody L., Idris, Ahamed H., Kiragu, Andrew, Mahgoub, Melissa, Shenoi, Rohit P., Szpilman, David, Terry, Mark, Tijssen, Janice A., Tobin, Joshua M., and Topjian, Alexis A.

Published
2024
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22. Cathepsin S Alterations Induce a Tumor-Promoting Immune Microenvironment in Follicular Lymphoma

Author

Bararia, Deepak, Hildebrand, Johannes A., Stolz, Sebastian, Haebe, Sarah, Alig, Stefan, Trevisani, Christopher P., Osorio-Barrios, Francisco, Bartoschek, Michael D., Mentz, Michael, Pastore, Alessandro, Gaitzsch, Erik, Heide, Michael, Jurinovic, Vindi, Rautter, Katharina, Gunawardana, Jay, Sabdia, Muhammed B., Szczepanowski, Monika, Richter, Julia, Klapper, Wolfram, Louissaint, Abner, Jr., Ludwig, Christina, Bultmann, Sebastian, Leonhardt, Heinrich, Eustermann, Sebastian, Hopfner, Karl-Peter, Hiddemann, Wolfgang, von Bergwelt-Baildon, Michael, Steidl, Christian, Kridel, Robert, Tobin, Joshua W.D., Gandhi, Maher K., Weinstock, David M., Schmidt-Supprian, Marc, Sárosi, Menyhárt B., Rudelius, Martina, Passerini, Verena, Mautner, Josef, and Weigert, Oliver

Published
2020
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23. LAG3: a novel immune checkpoint expressed by multiple lymphocyte subsets in diffuse large B-cell lymphoma

Author

Keane, Colm, Law, Soi C., Gould, Clare, Birch, Simone, Sabdia, Muhammed B., Merida de Long, Lilia, Thillaiyampalam, Gayathri, Abro, Emad, Tobin, Joshua W., Tan, Xiaohong, Xu-Monette, Zijun Y., Young, Ken H., Gifford, Grace, Gabreilli, Sara, Stevenson, William S., Gill, Anthony, Talaulikar, Dipti, Jain, Sanjiv, Hernandez, Annette, Halliday, Sarah-Jane, Bird, Robert, Cross, Donna, Hertzberg, Mark, and Gandhi, Maher K.

Published
2020
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24. Identifying comorbidity patterns of mental health disorders in community-dwelling older adults: A cluster analysis

Author

Tobin, Joshua, primary, Black, Michaela, additional, Ng, James, additional, Rankin, Debbie, additional, Wallace, Jonathan, additional, Hughes, Catherine, additional, Hoey, Leane, additional, Moore, Adrian, additional, Wang, Jinling, additional, Horigan, Geraldine, additional, Carlin, Paul, additional, McNulty, Helene, additional, Molloy, Anne M, additional, and Zhang, Mimi, additional

Published
2024
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26. Knot Floer Filtration Classes of Topologically Slice Knots

Author

Tobin, Joshua

Subjects
Mathematics - Geometric Topology
Abstract

The knot Floer complex and the concordance invariant $\varepsilon$ can be used to define a filtration on the smooth concordance group. We exhibit an ordered subset of this filtration that is isomorphic to $\mathbb{N} \times \mathbb{N}$ and consists of topologically slice knots., Comment: 22 pages, 1 figure

Published
2013

27. Defining Combat-Relevant Endpoints for Early Trauma Resuscitation Research in a Resource-Constrained Civilian Setting

Author

Mould-Millman, Nee-Kofi, Mata, Lina, Schauer, Steven G., Dixon, Julia, Keenan, Sean, Holcomb, John B., Tobin, Joshua M., Moore, E., de Vries, Shaheem, Bedard, Alexander, Bebarta, Vikhyat S., and Ginde, Adit A.

Subjects
United States. Department of Defense, University of Colorado, Injuries, Mortality -- South Africa -- United Kingdom, Civilians -- Injuries, Brain injuries, Brain -- Injuries
Abstract

BACKGROUND Trauma continues to be a leading cause of global morbidity and mortality in military and civilian populations alike. (1,2) Among US combat-wounded personnel, hemorrhage causes the overwhelming majority of [...]

Published
2021

28. Outcomes of stage I/II follicular lymphoma in the PET era: an international study from the Australian Lymphoma Alliance

Author

Tobin, Joshua W.D., Rule, Gabrielle, Colvin, Katherine, Calvente, Lourdes, Hodgson, David, Bell, Stephen, Dunduru, Chengetai, Gallo, James, Tsang, Erica S., Tan, Xuan, Wong, Jonathan, Pearce, Jessica, Campbell, Robert, Tneh, Shao, Shorten, Sophie, Ng, Melissa, Cochrane, Tara, Tam, Constantine S., Abro, Emad, Hawkes, Eliza, Hodges, Georgina, Kansara, Roopesh, Talaulikar, Dipti, Gilbertson, Michael, Johnston, Anna M., Savage, Kerry J., Villa, Diego, Morris, Kirk, Ratnasingam, Sumi, Janowski, Wojt, Kridel, Robert, Cheah, Chan Y., MacManus, Michael, Matigian, Nicholas, Mollee, Peter, Gandhi, Maher K., and Hapgood, Greg

Published
2019
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30. 2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces

Author

Berg, Katherine M., primary, Bray, Janet E., additional, Ng, Kee-Chong, additional, Liley, Helen G., additional, Greif, Robert, additional, Carlson, Jestin N., additional, Morley, Peter T., additional, Drennan, Ian R., additional, Smyth, Michael, additional, Scholefield, Barnaby R., additional, Weiner, Gary M., additional, Cheng, Adam, additional, Djärv, Therese, additional, Abelairas-Gómez, Cristian, additional, Acworth, Jason, additional, Andersen, Lars W., additional, Atkins, Dianne L., additional, Berry, David C., additional, Bhanji, Farhan, additional, Bierens, Joost, additional, Bittencourt Couto, Thomaz, additional, Borra, Vere, additional, Böttiger, Bernd W., additional, Bradley, Richard N., additional, Breckwoldt, Jan, additional, Cassan, Pascal, additional, Chang, Wei-Tien, additional, Charlton, Nathan P., additional, Chung, Sung Phil, additional, Considine, Julie, additional, Costa-Nobre, Daniela T., additional, Couper, Keith, additional, Dainty, Katie N., additional, Dassanayake, Vihara, additional, Davis, Peter G., additional, Dawson, Jennifer A., additional, Fernanda de Almeida, Maria, additional, De Caen, Allan R., additional, Deakin, Charles D., additional, Dicker, Bridget, additional, Douma, Matthew J., additional, Eastwood, Kathryn, additional, El-Naggar, Walid, additional, Fabres, Jorge G., additional, Fawke, Joe, additional, Fijacko, Nino, additional, Finn, Judith C., additional, Flores, Gustavo E., additional, Foglia, Elizabeth E., additional, Folke, Fredrik, additional, Gilfoyle, Elaine, additional, Goolsby, Craig A., additional, Granfeldt, Asger, additional, Guerguerian, Anne-Marie, additional, Guinsburg, Ruth, additional, Hatanaka, Tetsuo, additional, Hirsch, Karen G., additional, Holmberg, Mathias J., additional, Hosono, Shigeharu, additional, Hsieh, Ming-Ju, additional, Hsu, Cindy H., additional, Ikeyama, Takanari, additional, Isayama, Tetsuya, additional, Johnson, Nicholas J., additional, Kapadia, Vishal S., additional, Daripa Kawakami, Mandira, additional, Kim, Han-Suk, additional, Kleinman, Monica E., additional, Kloeck, David A., additional, Kudenchuk, Peter, additional, Kule, Amy, additional, Kurosawa, Hiroshi, additional, Lagina, Anthony T., additional, Lauridsen, Kasper G., additional, Lavonas, Eric J., additional, Lee, Henry C., additional, Lin, Yiqun, additional, Lockey, Andrew S., additional, Macneil, Finlay, additional, Maconochie, Ian K., additional, John Madar, R., additional, Malta Hansen, Carolina, additional, Masterson, Siobhan, additional, Matsuyama, Tasuku, additional, McKinlay, Christopher J.D., additional, Meyran, Daniel, additional, Monnelly, Vix, additional, Nadkarni, Vinay, additional, Nakwa, Firdose L., additional, Nation, Kevin J., additional, Nehme, Ziad, additional, Nemeth, Michael, additional, Neumar, Robert W., additional, Nicholson, Tonia, additional, Nikolaou, Nikolaos, additional, Nishiyama, Chika, additional, Norii, Tatsuya, additional, Nuthall, Gabrielle A., additional, Ohshimo, Shinchiro, additional, Olasveengen, Theresa M., additional, Gene Ong, Yong-Kwang, additional, Orkin, Aaron M., additional, Parr, Michael J., additional, Patocka, Catherine, additional, Perkins, Gavin D., additional, Perlman, Jeffrey M., additional, Rabi, Yacov, additional, Raitt, James, additional, Ramachandran, Shalini, additional, Ramaswamy, Viraraghavan V., additional, Raymond, Tia T., additional, Reis, Amelia G., additional, Reynolds, Joshua C., additional, Ristagno, Giuseppe, additional, Rodriguez-Nunez, Antonio, additional, Roehr, Charles C., additional, Rüdiger, Mario, additional, Sakamoto, Tetsuya, additional, Sandroni, Claudio, additional, Sawyer, Taylor L., additional, Schexnayder, Steve M., additional, Schmölzer, Georg M., additional, Schnaubelt, Sebastian, additional, Semeraro, Federico, additional, Singletary, Eunice M., additional, Skrifvars, Markus B., additional, Smith, Christopher M., additional, Soar, Jasmeet, additional, Stassen, Willem, additional, Sugiura, Takahiro, additional, Tijssen, Janice A., additional, Topjian, Alexis A., additional, Trevisanuto, Daniele, additional, Vaillancourt, Christian, additional, Wyckoff, Myra H., additional, Wyllie, Jonathan P., additional, Yang, Chih-Wei, additional, Yeung, Joyce, additional, Zelop, Carolyn M., additional, Zideman, David A., additional, Nolan, Jerry P., additional, Barcala-Furelos, Roberto, additional, Beerman, Stephen B., additional, Bruckner, Marlies, additional, Castrén, Maaret, additional, Chong, ShuLing, additional, Claesson, Andreas, additional, Dunne, Cody L., additional, Finan, Emer, additional, Fukuda, Tatsuma, additional, Lalgudi Ganesan, Saptharishi, additional, Gately, Callum, additional, Gois, Aecio, additional, Gray, Seth, additional, Halamek, Louis P., additional, Hoover, Amber V., additional, Hurst, Cameron, additional, Josephsen, Justin, additional, Kollander, Louise, additional, Omar Kamlin, C., additional, Kool, Mirjam, additional, Li, Lei, additional, Mecrow, Thomas S., additional, Montgomery, William, additional, Ristau, Patrick, additional, Jayashree, Muralidharan, additional, Schmidt, Andrew, additional, Scquizzato, Tommaso, additional, Seesink, Jeroen, additional, Sempsrott, Justin, additional, Lee Solevåg, Anne, additional, Strand, Marya L., additional, Szpilman, David, additional, Szyld, Edgardo, additional, Thom, Ogilvie, additional, Tobin, Joshua M., additional, Trang, Jacinta, additional, Webber, Jonathon, additional, Webster, Hannah K., additional, and Wellsford, Michelle, additional

Published
2024
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32. Diagnosis, management and follow‐up of follicular lymphoma: a consensus practice statement from the Australasian Lymphoma Alliance.

Author

Tobin, Joshua W. D., Hapgood, Greg, Johnston, Anna, Cheah, Chan Y., Lee, Sze T., Trotman, Judith, Inam, Shafqat, Campbell, Belinda A., Norris, Debbie, MacManus, Michael, Hertzberg, Mark, and Hawkes, Eliza

Subjects
*CONSENSUS (Social sciences), *NON-Hodgkin's lymphoma, *CANCER relapse, *DISEASE management, *MEDICAL societies, *CHEMORADIOTHERAPY, *DISEASE remission, *TUMOR classification, *EVIDENCE-based medicine, *PATIENT aftercare, *DISEASE progression
Abstract

Follicular lymphoma (FL) is the most common indolent non‐Hodgkin lymphoma subtype, accounting for 15–20% of all lymphoma diagnoses. Although typically slow‐growing and responsive to frontline therapies, advanced‐stage FL remains incurable with current treatments and typically follows a chronic relapsing/remitting course with increasingly shorter responses to subsequent lines of therapy. Outcomes are highly variable; some patients experience prolonged first remissions that may approximate a 'functional cure'. By contrast, a significant minority of patients experience disease progression shortly after frontline treatment resulting in high rates of lymphoma‐related mortality. Reflecting on the heterogeneous natural history of FL, clinical practice varies widely, particularly in controversial areas, including appropriate disease staging, selection of management strategies and duration of clinical follow‐up. This position statement presents an evidence‐based synthesis of the literature for application in Australasian practice. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Damage Control Anesthesia

Author

Sugeir, Shihab, Grunstein, Itamar, Tobin, Joshua M., Duchesne, Juan, editor, Inaba, Kenji, editor, and Khan, Mansoor Ali, editor

Published
2018
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38. Analysis of Risk Factors and Diagnosis for Anxiety Disorder in Older People with the Aid of Artificial Intelligence: Observational Study

Author

Wang, Jinling, primary, Black, Michaela, additional, Rankin, Debbie, additional, Wallace, Jonathan, additional, Hughes, Catherine F, additional, Hoey, Leane, additional, Moore, Adrian, additional, Tobin, Joshua, additional, Zhang, Mimi, additional, Ng, James, additional, Horigan, Geraldine, additional, Carlin, Paul, additional, McCarroll, Kevin, additional, Cunningham, Conal, additional, McNulty, Helene, additional, and Molloy, Anne M, additional

Published
2023
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46. Epstein–Barr virus‐associated lymphomas decoded.

Author

Bednarska, Karolina, Chowdhury, Rakin, Tobin, Joshua W. D., Swain, Fiona, Keane, Colm, Boyle, Stephen, Khanna, Rajiv, and Gandhi, Maher K.

Subjects
LYMPHOMAS, HODGKIN'S disease, LYMPHOPROLIFERATIVE disorders, TUMOR microenvironment, EPSTEIN-Barr virus
Abstract

Summary: Epstein–Barr virus (EBV)‐associated lymphomas cover a range of histological B‐ and T‐cell non‐Hodgkin and Hodgkin lymphoma subtypes. The role of EBV on B‐cell malignant pathogenesis and its impact on the tumour microenvironment are intriguing but incompletely understood. Both the International Consensus Classification (ICC) and 5th Edition of the World Health Organization (WHO‐HAEM5) proposals give prominence to the distinct clinical, prognostic, genetic and tumour microenvironmental features of EBV in lymphoproliferative disorders. There have been major advances in our biological understanding, in how to harness features of EBV and its host immune response for targeted therapy, and in using EBV as a method to monitor disease response. In this article, we showcase the latest developments and how they may be integrated to stimulate new and innovative approaches for further lines of investigation and therapy. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Real-World Robotic Perception and Control Using Synthetic Data

Author

Tobin, Joshua P

Subjects
Computer science, Artificial intelligence, Robotics, Deep learning, Domain randomization, Sim-to-real transfer, Synthetic data
Abstract

Modern deep learning techniques are data-hungry, which presents a problem in robotics because real-world robotic data is difficult to collect. Simulated data is cheap and scalable, but jumping the "reality gap" to use simulated data for real-world tasks is challenging. In this thesis, we discuss using synthetic data to learn visual models that allow robots to perform manipulation tasks in the real world. We begin by discussing domain randomization, a technique for bridging the reality gap by massively randomizing the visual properties of the simulator. We demonstrate that, using domain randomization, synthetic data alone can be used to train a deep neural network to localize objects accurately enough for a robot to grasp them in the real world. The remainder of the thesis discusses extensions of this approach to a broader range of objects and scenes. First, we introduce a data generation pipeline inspired by the success of domain randomization for visual data that creates millions of unrealistic procedurally generated random objects, removing the assumption that 3D models of the objects are present at training time. Second, we reformulate the problem from pose prediction to grasp prediction and introduce a generative model architecture that learns a distribution over grasps, allowing our models to handle pose ambiguity and grasp a wide range of objects with a single neural network. Third, we introduce an attention mechanism for 3-dimensional data. We demonstrate that this attention mechanism can be used to perform higher fidelity neural rendering, and that models learned this way can be fine-tuned to perform accurate pose estimation when the camera intrinsics are unknown at training time.We conclude by surveying recent applications and extensions of domain randomization in the literature and suggesting several promising directions for research in sim-to-real transfer for robotics.

Published
2019

50. A Theoretical Analysis of Density Peaks Clustering and the Component-Wise Peak-Finding Algorithm

Author

Tobin, Joshua and Zhang, Mimi

Abstract

Density peaks clustering detects modes as points with high density and large distance to points of higher density. Each non-mode point is assigned to the same cluster as its nearest neighbor of higher density. Density peaks clustering has proved capable in applications, yet little work has been done to understand its theoretical properties or the characteristics of the clusterings it produces. Here, we prove that it consistently estimates the modes of the underlying density and correctly clusters the data with high probability. However, noise in the density estimates can lead to erroneous modes and incoherent cluster assignments. A novel clustering algorithm, Component-wise Peak-Finding (CPF), is proposed to remedy these issues. The improvements are twofold: 1) the assignment methodology is improved by applying the density peaks methodology within level sets of the estimated density; 2) the algorithm is not affected by spurious maxima of the density and hence is competent at automatically deciding the correct number of clusters. We present novel theoretical results, proving the consistency of CPF, as well as extensive experimental results demonstrating its exceptional performance. Finally, a semi-supervised version of CPF is presented, integrating clustering constraints to achieve excellent performance for an important problem in computer vision.

Published
2024
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