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303. Lack of regional pathways impact on surgical delay: Analysis of the Orthopaedic Trauma Hospital Outcomes–Patient Operative Delays (ORTHOPOD) study

Author

Stevenson, Iain, Yoong, Andrel, Rankin, Iain, Dixon, James, Lim, Jun Wei, Sattar, Mariam, McDonald, Stephen, Scott, Sharon, Davies, Helen, Jones, Louise, Nolan, Michelle, McGinty, Rebecca, Stevenson, Helene, Bowe, David, Sim, Francis, Vun, James, Strain, Ritchie, Giannoudis, Vasileios, Talbot, Christopher, Gunn, Christopher, Le, Ha Phuong Do, Bradley, Matthew, Lloyd, William, Hanratty, Brian, Lim, Yizhe, Brookes-Fazakerley, Steven, Varasteh, Amir, Francis, Jonathan, Choudhry, Nameer, Malik, Sheraz, Vats, Amit, Evans, Ashish, Garner, Madeleine, King, Stratton, Zbaeda, Mohamed, Diamond, Owen, Baker, Gavin, Napier, Richard, Guy, Stephen, McCauley, Gordon, King, Samuel, Edwards, Gray, Lin, Benjamin, Davoudi, Kaveh, Haines, Samuel, Raghuvanshi, Manav, Buddhdev, Pranai, Karam, Edward, Nimmyel, Enoch, Ekanem, George, Lateef, Razaq, Jayadeep, JS, Crowther, Ian, Mazur, Karolina, Hafiz, Nauman, Khan, Umair, Chettiar, Krissen, Ibrahim, Amr, Gopal, Prasanth, Tse, Shannon, Lakshmipathy, Raj, Towse, Claudia, Al-Musawi, Hashim, Walmsley, Matthew, Aspinall, Will, Metcalfe, James, Moosa, Aliabbas, Crome, George, Abdelmonem, Mohamed, Lakpriya, Sathya, Hawkins, Amanda, Waugh, Dominic, Kennedy, Matthew, Elsagheir, Mohamed, Kieffer, Will, Oyekan, Adekinte, Collis, Justin, Raad, Marjan, Raut, Pramin, Baker, Markus, Gorvett, Alexander, Gleeson, Hannah, Fahmy, John, Walters, Sam, Tinning, Craig, Chaturvedi, Abhishek, Russell, Heather, Alsawada, Osama, Sinnerton, Robert, Crane, Evan, Warwick, Catherine, Dimascio, Lucia, Ha, Taegyeong Tina, King, Thomas, Engelke, Daniel, Chan, Matthew, Gopireddy, Rajesh, Deo, Sunny, Vasarhelyi, Ferenc, Jhaj, Jasmeet, Dogramatzis, Kostas, McCartney, Sarah, Ardolino, Toni, Fraig, Hossam, Hiller-Smith, Ryan, Haughton, Benjamin, Greenwood, Heather, Stephenson, Nicola, Chong, Yuki, Sleat, Graham, Saedi, Farid, Gouda, Joe, Ravi, Sanjeev Musuvathy, Henari, Shwan, Imam, Sam, Howell, Charles, Theobald, Emma, Wright, Jan, Cormack, Jonathan, Borja, Karlou, Wood, Sandy, Khatri, Amulya, Bretherton, Chris, Tunstall, Charlotte, Lowery, Kathryn, Holmes, Benjamin, Nichols, Jennifer, Bashabayev, Beibit, Wildin, Clare, Sofat, Rajesh, Thiagarajan, Aarthi, Abdelghafour, Karim, Nicholl, James, Abdulhameed, Ahmed, Duke, Kathryn, Maling, Lucy, McCann, Matthew, Masud, Saqib, Marshman, James, Moreau, Joshua, Cheema, Kanwalnaini, Rageeb, Peter Morad, Mirza, Yusuf, Kelly, Andrew, Hassan, Abdul, Christie, Alexander, Davies, Angharad, Tang, Cary, Frostick, Rhiannon, Pemmaraju, Gopalakrishna, Handford, Charles, Chauhan, Govind, Dong, Huan, Choudri, Mohammed Junaid, Loveday, David, Bawa, Akshdeep, Baldwick, Cheryl, Roberton, Andrew, Burden, Eleanor, Nagi, Sameer, Johnson-Lynn, Sarah, Guiot, Luke, Kostusiak, Milosz, Appleyard, Thomas, Mundy, Gary, Basha, Amr, Abdeen, Bashar, Robertson-Smith, Bill, Hussainy, Haydar Al, Reed, Mike, Jamalfar, Aral, Flintoft, Emily, McGovern, Julia, Alcock, Liam, Koziara, Michal, Ollivere, Benjamin, Zheng, Amy, Atia, Fady, Goff, Thomas, Slade, Henry, Teoh, Kar, Shah, Nikhil, Al-Obaedi, Ossama, Jamal, Bilal, Bell, Stuart, Macey, Alistair, Brown, Cameron, Simpson, Cameron, Alho, Roberto, Wilson, Victoria, Lewis, Charlotte, Blyth, Daniel, Chapman, Laura, Woods, Lisa, Katmeh, Rateb, Pasapula, Chandra, Youssef, Hesham, Tan, Jerry, Famure, Steven, Grazette, Andrew, Lloyd, Adam, Beaven, Alastair, Jackowski, Anna, Piper, Dani, Lotfi, Naeil, Chakravarthy, Jagannath, Elzawahry, Ahmed, Trew, Christopher, Neo, Chryssa, Elamin-Ahmed, Hussam, Ashwood, Neil, Wembridge, Kevin, Eyre-Brook, Alistair, Greaves, Amy, Watts, Anna, Stedman, Tobias, Ker, Andrew, Wong, Li Siang, Fullarton, Mairi, Phelan, Sean, Choudry, Qaisar, Qureshi, Alham, Moulton, Lawrence, Cadwallader, Craig, Jenvey, Cara, Aqeel, Aqeel, Francis, Daniel, Simpson, Robin, Phillips, Jon, Matthews, Edward, Thomas, Ellen, Williams, Mark, Jones, Robin, White, Tim, Ketchen, Debbie, Bell, Katrina, Swain, Keri, Chitre, Amol, Lum, Joann, Syam, Kevin, Dupley, Leanne, O'Brien, Sarah, Ford, David, Chapman, Taya, Zahra, Wajiha, Guryel, Enis, McLean, Elizabeth, Dhaliwal, Kawaljit, Regan, Nora, Berstock, James, Deano, Krisna, Donovan, Richard, Blythe, Andrew, Salmon, Jennifer, Craig, Julie, Hickland, Patrick, Matthews, Scott, Brown, William, Borland, Steven, Aminat, Akinsemoyin, Stamp, Gregory, Zaheen, Humayoon, Jaibaji, Monketh, Egglestone, Anthony, Sampalli, Sridhar Rao, Goodier, Henry, Gibb, Julia, Islam, Saad, Ranaboldo, Tom, Theivendran, Kanthan, Bond, Georgina, Richards, Joanna, Sanghera, Ranjodh, Robinson, Karen, Fong, Angus, Tsang, Bonita, Dalgleish, James, McGregor-Riley, Jonathan, Barkley, Sarah, Eardley, William, Elhassan, Almutasim, Tyas, Ben, Chandler, Henry, McVie, James, Wei, Nicholas, Negus, Oliver, Baldock, Thomas, Ravi, Kuppuswamy, Qazzaz, Layth, Mohamed, Muawia, Sivayoganthan, Sriharan, Poole, William, Slade, George, Beaumont, Hugo, Beaumont, Oliver, Taha, Rowa, Lever, Caroline, Sood, Abhay, Moss, Maximillian, Khatir, Mohammed, Trompeter, Alex, Jeffers, Aisha, Brookes, Charlotte, Dadabhoy, Maria, Bhattacharya, Rajarshi, Singh, Abhinav, Beer, Alexander, Hodgson, Harry, Rahman, Kashed, Barter, Reece, Mackinnon, Thomas, Frasquet-Garcia, Antonio, Aldarragi, Ameer, Warner, Christian, Pantelides, Christopher, Attwood, Joseph, Al-Uzri, Muntadhir, Qaoud, Qaiys Abu, Green, Stephen, Osborne, Alex, Griffiths, Alexandra, Emmerson, Benjamin, Slater, Duncan, Altahoo, Hasan, Scott, Helen, Rowland, David, O'Donnell, Janine, Edwards, Taff, Hafez, Ahmed, Khan, Basharat, Crane, Emily, Axenciuc, Rostislav, Al-Habsi, Ruqaiya, McAlinden, Gavan, Sterne, Jonathan, Wong, Matthew Lynch, Patil, Sunit, Ridha, Ali, Rasidovic, Damir, Searle, Henry, Choudhry, Jamaal, Farhan-Alanie, Muhamed M, Tanagho, Andy, Sharma, Sidharth, Thomas, Suresh, Smith, Ben, McMullan, Mark, Winstanley, Robert, Mirza, Saqeb, Hamlin, Katharine, Elgayar, Lugman, Larsen, Matthew P, Eissa, Mohamed, Stevens, Samuel, Hopper, Graeme P, Fang Soh, Terrence Chi, Doorgakant, Ashtin, Yogeswaran, Apimaan, Myatt, Darren, Mahon, Joseph, Ward, Nicholas, Reid, Susan, Deierl, Krisztian, Brogan, Declan, Little, Max, Deakin, Sue, Baines, Elliott, Jones, Georgie, Boulton, Helen, Douglas, Trixie, Jeyaseelan, Lucky, Abdale, Abdirizak, Islam, Aminul, Atkinson, Kate V, Mohamedfaris, Khalid, Mmerem, Kingsley, Jamal, Shazil, Wharton, Danielle, Rana, Anurag, McAllister, Ross, Sasi, Sijith, Thomas, Terin, Pillai, Anand, Flaherty, David, Khan, Munir, Akkena, Sudheer, Shandala, Yaseen, Lankester, Benedict, Hainsworth, Louis, Ahmed, Hussam Elamin, Walshaw, Thomas, Walker, Reece, and Eardley, William G.P.

Published
2023
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306. Higher Survival With the Use of Extracorporeal Cardiopulmonary Resuscitation Compared With Conventional Cardiopulmonary Resuscitation in Children Following Cardiac Surgery: Results of an Analysis of the Get With The Guidelines-Resuscitation Registry

Author

Kobayashi, Ryan L., Gauvreau, Kimberlee, Alexander, Peta M. A., Teele, Sarah A., Fynn-Thompson, Francis, Lasa, Javier J., Bembea, Melania, Thiagarajan, Ravi R., Guerguerian, Anne-Marie, Fink, Ericka L., Roberts, Joan S., Su, Lillian, Brown, Linda L., Dewan, Maya, Kleinman, Monica, Gupta, Punkaj, Sutton, Robert M., Reeder, Ron, and Sweberg, Todd

Published
2023
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309. Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors

Author

Sattigeri, Prasanna, Thiagarajan, Jayaraman, Ramamurthy, Karthikeyan, Spanias, Andreas, Banavar, Mahesh, Dixit, Abhinav, Fan, Jie, Malu, Mohit, Jaskie, Kristen, Rao, Sunil, Shanthamallu, Uday, and Katoch, Sameeksha

Abstract

Ion channel sensors have several applications including DNA sequencing, biothreat detection, and medical applications. Ion channel sensors mimic the selective transport mechanism of cell membranes and can detect a wide range of analytes at the molecule level. Analytes are sensed through changes in signal patterns. Papers in the literature have described different methods for ion channel signal analysis. In this paper, the authors describe a series of new graphical tools for ion channel signal analysis which can be used for research and education. The paper focuses on the utility of these tools in biosensor classes. Teaching signal processing and machine learning for ion channel sensors is challenging because of the multidisciplinary content and student backgrounds which include physics, chemistry, biology, and engineering. The paper describes graphical ion channel analysis tools developed for an online simulation environment called J-DSP. The tools are integrated and assessed in a graduate bio-sensor course through computer laboratory exercises.

Published
2022
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317. Federated benchmarking of medical artificial intelligence with MedPerf.

Author

Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Anna Wuest, Sarthak Pati, Hasan Kassem, Maximilian Zenk, Ujjwal Baid, Prakash Narayana Moorthy, Alexander Chowdhury, Junyi Guo, Sahil S. Nalawade, Jacob Rosenthal, David Kanter, Maria Xenochristou, Daniel J. Beutel, Verena Chung, Timothy Bergquist, James A. Eddy, Abubakar Abid, Lewis Tunstall, Omar Sanseviero, Dimitrios Dimitriadis, Yiming Qian, Xinxing Xu, Yong Liu 0026, Rick Siow Mong Goh, Srini Bala, Victor Bittorf, Sreekar Reddy Puchala, Biagio Ricciuti, Soujanya Samineni, Eshna Sengupta, Akshay Chaudhari, Cody Coleman, Bala Desinghu, Gregory F. Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Xinyuan Huang, Satyananda Kashyap, Nicholas D. Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Cassiano Ferro Moraes, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G. Anthony Reina, Pablo Ribalta, Abhishek Singh, Jayaraman J. Thiagarajan, Jacob Albrecht, Thomas Wolf 0008, Geralyn Miller, Huazhu Fu, Prashant Shah, Daguang Xu, Poonam Yadav, David Talby, Mark M. Awad, Jeremy P. Howard, Michael Rosenthal, Luigi Marchionni, Massimo Loda, Jason M. Johnson, Spyridon Bakas, and Peter Mattson

Published
2023
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319. RETRACTED: Tzeyung et al. Fabrication, Optimization, and Evaluation of Rotigotine-Loaded Chitosan Nanoparticles for Nose-To-Brain Delivery. Pharmaceutics 2019, 11, 26

Author

Angeline Shak. Tzeyung, Shadab Md, Subrat Kumar Bhattamisra, Thiagarajan Madheswaran, Nabil A. Alhakamy, Hibah M. Aldawsari, and Ammu K. Radhakrishnan

Subjects
n/a, Pharmacy and materia medica, RS1-441
Abstract

The journal retracts the article, “Fabrication, Optimization, and Evaluation of Rotigotine-Loaded Chitosan Nanoparticles for Nose-To-Brain Delivery” [...]

Published
2024
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320. 167 An Evaluation of Altmetric Attention using Network Science and Natural Language Processing

Author

Alaguvalliappan Thiagarajan, Christopher McCarty, and Edward Seh-Taylor

Subjects
Medicine
Abstract

OBJECTIVES/GOALS: Our project aims to assess the composition or characteristics of research papers that score high on alternative metrics. These alternative metrics including the number of newspaper mentions, social media mentions, and the attention score as catalogued on Altmetric, a tool used to document community attention for a given research paper. METHODS/STUDY POPULATION: Our study intends to 1) Utilize topic modeling to identify prevalent themes on Altmetric, and 2) Apply network analysis to elucidate the interconnectedness among universities, funding sources, journals, and publishers associated with high-attention papers. 3) Examine how these patterns vary when attention metrics shift, such as social media mentions, newspaper mentions, or the Altmetric score. We'll first perform this analysis on all types of papers and then limit the networks to Biomedical and Clinical Sciences, and Public and Allied Health Sciences to help inform what health topics garner attention. RESULTS/ANTICIPATED RESULTS: Our initial Altmetric topic models revealed sustained attention for COVID-19 and vaccination-related publications well beyond the pandemic (specifically, papers from January 2023). Health topics like cancer, dementia, and obesity also garnered high attention. Additionally, political papers (elections, democracy), climate change, and battery research had notable attention values. Further analysis needs to be done to explain why these topics gain attention and the type of attention they garner. We will construct networks to see the relationship between attention and entities like universities, funding sources, journals, and publishers. This will identify whether certain clusters of these entities produce papers with high attention or if attention is distributed evenly amoung them. DISCUSSION/SIGNIFICANCE: To gauge the broader impact of scholarly research alternative metrics beyond citations are needed. Altmetric is used widely by CTSA’s to measure the community interest in research. Understanding the types of research that gain traction on Altmetric can help researchers understand how to garner interest from the community.

Published
2024
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321. Enhanced corrosion inhibition effect of sodium tartrate on copper in potable water

Author

Ramasamy Sudhakaran, Thiagarajan Deepa, Munusamy Thirumavalavan, Sharmila Queenthy Sabarimuthu, Sellamuthu Babu, Thayuman Asokan, Abdulrahman I. Almansour, Pandian Bothi Raja, and Karthikeyan Perumal

Subjects
Corrosion, Copper, Electrochemical studies, SEM, EDAX, AFM, Science (General), Q1-390
Abstract

In this study we have reported sodium tartrate (ST) and Zn2+ as the potential mixed corrosion inhibitors for copper corrosion in drinking water, by using electrochemical impedance and polarization techniques. The results of potentio-dynamic polarization indicated that sodium tartrate could be used as mixed type inhibitor with Zn2+. ST was found to be 92% effective for slowing down both the anodic and cathodic reaction rates. It was additionally found that ST could coat the surface of copper to prevent it from conducting electricity. As the inhibitor concentration increased, the stability of the formed protective layer was also improved. The results obtained from studies like SEM, EDAX, AFM, and water contact angle clearly indicated the development of a barrier by inducing the lotus effect on copper surface. The water contact angle measurement results suggested that the coating formed in the presence of inhibitor was superhydrophobic, and the surface was homogeneous.

Published
2023
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322. Performing Joget Gamelan through archives and social memory

Author

Jafar Norsafini and Thiagarajan Premalatha

Subjects
Malay, classical dance, Joget Gamelan, archives, social memory, Fine Arts, Arts in general, NX1-820, General Works, History of scholarship and learning. The humanities, AZ20-999
Abstract

This study explores the role of archives and social memory in preserving the Malay classical dance, Joget Gamelan. Using ethnographic methods, it examines the impact of archives and repertoire on this dance form. Joget Gamelan originated from Riau Lingga and later spread to the Malaysian states of Johor, Pahang, and Terengganu, where it was performed during royal ceremonies since the 19th century. Its popularity peaked in the 1920s when it was recorded in the Tengku Ampuan Mariam manuscript for preservation. However, as time passed, the dance relied on the “memory” and embodied experiences of master teachers such as Zaharah Abdul Hamid and Wan Salmah Sulaiman, as the manuscript was inaccessible. The preservation through time was dependent on the social memory of female teachers. This study employs Paul Connerton’s “social memory” to explore the changes in the dance form. The teachers reconstructed the dance to meet contemporary demands, and Diana Taylor’s concepts of “archives” and “repertoire” are used to investigate the reconstruction of Joget Gamelan for staged performances. This study does not criticize the current practice or identify shortcomings but rather seeks to investigate the “absences” in the form, which can be recovered through archival sources like old manuscripts, poems, and dance notations. By revisiting the past through these sources, the dance form is expected to reclaim its “forgotten” or “lost” aesthetics. The study argues that the intersection of social memory and archives is crucial in preserving the dance’s originality and re-connecting it with the tradition.

Published
2023
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323. Trisodium citrate as a potential and eco-friendly corrosion inhibitor of copper in potable water

Author

Ramasamy Sudhakaran, Thiagarajan Deepa, Munusamy Thirumavalavan, Sharmila Queenthy Sabarimuthu, Sellamuthu Babu, Thayuman Asokan, Pandian Bothi Raja, Natarajan Arumugam, Karthikeyan Perumal, Sinouvassane Djearamane, Lai-Hock Tey, and Saminathan Kayarohanam

Subjects
Corrosion, Sodium citrate, Binary inhibitor, Super hydrophobicity, Electrochemical studies, Science (General), Q1-390
Abstract

In this work, the mixture of trisodium cittrate and Zn2+ was used as binary (hetero type) inhibitor for corrosion inhibition of copper metal in potable water. The binary inhibitor system (Zn2+ and trisodium citrate) was used to form hydrophobic surfaces on copper submerged in potable water. Water contact angle (WCA) was found to be 155°4° when the inhibitor was present, whereas it was 84°2° when there was no inhibitor. These observations suggested the development of superhydrophobic layer on the surface of copper in drinkable water. Electrochemical impedance spectroscopy (EIS – AC mode), and potentiodynamic polarization (DC mode) experiments conveyed that the copper surface could be protected by utilizing the mixture of trisodium citrate and Zn2+ in potable water. The morphological studies including SEM (coupled with EDX), AFM, and WCA were evidenced the formulation of a hetero-type inhibitor for the corrosion inhibition of copper in potable water. In this study, the decline in the double-layer capacitance and the rise in the charge transfer resistance were due to the adsorption of inhibitor confirming the development of protective layer, which EIS, SEM, EDX, AFM, and WCA studies also supported. Thus, there was a synergism observed between TSC and Zn2+, and the formulation consisting of TSC and Zn2+ provided 83% of inhibition efficiency (IEp). So, it was suggested that the approach reported in this study could be a simple method for obtaining the superhydrophobic copper surface.

Published
2023
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324. A robust pipeline for high-content, high-throughput immunophenotyping reveals age- and genetics-dependent changes in blood leukocytes

Author

Thomas Liechti, Sofie Van Gassen, Margaret Beddall, Reid Ballard, Yaser Iftikhar, Renguang Du, Thiagarajan Venkataraman, David Novak, Massimo Mangino, Stephen Perfetto, H. Benjamin Larman, Tim Spector, Yvan Saeys, and Mario Roederer

Subjects
CP: Immunology, CP: Systems biology, Biotechnology, TP248.13-248.65, Biochemistry, QD415-436, Science
Abstract

Summary: High-dimensional flow cytometry is the gold standard to study the human immune system in large cohorts. However, large sample sizes increase inter-experimental variation because of technical and experimental inaccuracies introduced by batch variability. Our high-throughput sample processing pipeline in combination with 28-color flow cytometry focuses on increased throughput (192 samples/experiment) and high reproducibility. We implemented quality control checkpoints to reduce technical and experimental variation. Finally, we integrated FlowSOM clustering to facilitate automated data analysis and demonstrate the reproducibility of our pipeline in a study with 3,357 samples. We reveal age-associated immune dynamics in 2,300 individuals, signified by decreasing T and B cell subsets with age. In addition, by combining genetic analyses, our approach revealed unique immune signatures associated with a single nucleotide polymorphism (SNP) that abrogates CD45 isoform splicing. In summary, we provide a versatile and reliable high-throughput, flow cytometry-based pipeline for immune discovery and exploration in large cohorts. Motivation: Flow cytometry-based immunophenotyping studies are crucial in human immunology research. However, large sample sizes cause non-biological data variation, which impacts precision. We developed a sample processing and analysis pipeline for high-dimensional flow cytometry that focuses on sample throughput and incorporates stringent instrument standardization, staining protocols, quality controls, and unsupervised data analysis. These measures mitigate batch effect and experimental errors and increase data precision. With our pipeline we measured 3,357 samples in 19 experiments and obtained minimal non-biological variation, showcasing its usability for large immunophenotyping studies.

Published
2023
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326. Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin

Author

Aguet, François, Akiyama, Yo, An, Eunkyung, Anand, Shankara, Anurag, Meenakshi, Babur, Ozgun, Bavarva, Jasmin, Birger, Chet, Birrer, Michael, Calinawan, Anna, Cantley, Lewis C., Cao, Song, Carr, Steve, Ceccarelli, Michele, Chan, Daniel, Chinnaiyan, Arul, Cho, Hanbyul, Chowdhury, Shrabanti, Cieslik, Marcin, Clauser, Karl, Colaprico, Antonio, Zhou, Daniel Cui, da Veiga Leprevost, Felipe, Day, Corbin, Dhanasekaran, Mohan, Domagalski, Marcin, Dou, Yongchao, Druker, Brian, Edwards, Nathan, Ellis, Matthew, Selvan, Myvizhi Esai, Francis, Alicia, Getz, Gad, Gillette, Michael A., Robles, Tania Gonzalez, Gosline, Sara, Gümüş, Zeynep, Heiman, David, Hiltke, Tara, Hong, Runyu, Hostetter, Galen, Hu, Yingwei, Huang, Chen, Huntsman, Emily, Iavarone, Antonio, Jaehnig, Eric, Jewel, Scott, Ji, Jiayi, Jiang, Wen, Lee Johnson, Jared, Katsnelson, Lizabeth, Ketchum, Karen, Krug, Karsten, Kumar-Sinha, Chandan, Lei, Jonathan, Liao, Yuxing, Lindgren, Caleb, Liu, Tao, Liu, Wenke, Ma, Weiping, Rodrigues, Fernanda Martins, McKerrow, Wilson, Mesri, Mehdi, Nesvizhskii, Alexey I., Newton, Chelsea, Oldroyd, Robert, Omenn, Gilbert, Paulovich, Amanda, Petralia, Francesca, Pugliese, Pietro, Reva, Boris, Ruggles, Kelly, Rykunov, Dmitry, Satpathy, Shankha, Savage, Sara, Schadt, Eric, Schnaubelt, Michael, Schraink, Tobias, Shi, Zhiao, Smith, Dick, Song, Xiaoyu, Stathias, Vasileios, Storrs, Erik, Tan, Jimin, Terekhanova, Nadezhda, Thangudu, Ratna, Thiagarajan, Mathangi, Tignor, Nicole, Wang, Joshua, Wang, Liang-Bo, Wang, Pei, Wang, Ying (Cindy), Wen, Bo, Wu, Yige, Yao, Lijun, Yaron, Tomer M., Yi, Xinpei, Zhang, Bing, Zhang, Hui, Zhang, Qing, Zhang, Xu, Zhang, Zhen, Chan, Daniel W., Dhanasekaran, Saravana M., Schürer, Stephan, Smith, Richard D., Wyczalkowski, Matthew A., Liang, Wen-Wei, Lu, Rita Jui-Hsien, Jayasinghe, Reyka G., Foltz, Steven M., Porta-Pardo, Eduard, Geffen, Yifat, Wendl, Michael C., Lazcano, Rossana, Kolodziejczak, Iga, Song, Yizhe, Govindan, Akshay, Demicco, Elizabeth G., Li, Xiang, Li, Yize, Sethuraman, Sunantha, Payne, Samuel H., Fenyö, David, Rodriguez, Henry, Wiznerowicz, Maciej, Shen, Hui, Mani, D.R., Rodland, Karin D., Lazar, Alexander J., Robles, Ana I., and Ding, Li

Published
2023
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327. Proteogenomic insights suggest druggable pathways in endometrial carcinoma

Author

Antczak, Andrzej, Anurag, Meenakshi, Bauer, Thomas, Birger, Chet, Birrer, Michael J., Borucki, Melissa, Cai, Shuang, Calinawan, Anna, Carr, Steven A., Castro, Patricia, Cerda, Sandra, Chan, Daniel W., Chesla, David, Cieslik, Marcin P., Cottingham, Sandra, Dhir, Rajiv, Domagalski, Marcin J., Druker, Brian J., Duffy, Elizabeth, Edwards, Nathan J., Edwards, Robert, Ellis, Matthew J., Eschbacher, Jennifer, Fam, Mina, Fevrier-Sullivan, Brenda, Francis, Jesse, Freymann, John, Gabriel, Stacey, Getz, Gad, Gillette, Michael A., Godwin, Andrew K., Goldthwaite, Charles A., Grady, Pamela, Hafron, Jason, Hariharan, Pushpa, Hindenach, Barbara, Hoadley, Katherine A., Huang, Jasmine, Ittmann, Michael M., Johnson, Ashlie, Jones, Corbin D., Ketchum, Karen A., Kirby, Justin, Le, Toan, Ma'ayan, Avi, Madan, Rashna, Mareedu, Sailaja, McGarvey, Peter B., Modugno, Francesmary, Montgomery, Rebecca, Nyce, Kristen, Paulovich, Amanda G., Pruetz, Barbara L., Qi, Liqun, Richey, Shannon, Schadt, Eric E., Shutack, Yvonne, Singh, Shilpi, Smith, Michael, Tansil, Darlene, Thangudu, Ratna R., Tobin, Matt, Um, Ki Sung, Vatanian, Negin, Webster, Alex, Wilson, George D., Wright, Jason, Zaalishvili, Kakhaber, Zhang, Zhen, Zhao, Grace, Dou, Yongchao, Katsnelson, Lizabeth, Gritsenko, Marina A., Hu, Yingwei, Reva, Boris, Hong, Runyu, Wang, Yi-Ting, Kolodziejczak, Iga, Lu, Rita Jui-Hsien, Tsai, Chia-Feng, Bu, Wen, Liu, Wenke, Guo, Xiaofang, An, Eunkyung, Arend, Rebecca C., Bavarva, Jasmin, Chen, Lijun, Chu, Rosalie K., Czekański, Andrzej, Davoli, Teresa, Demicco, Elizabeth G., DeLair, Deborah, Devereaux, Kelly, Dhanasekaran, Saravana M., Dottino, Peter, Dover, Bailee, Fillmore, Thomas L., Foxall, McKenzie, Hermann, Catherine E., Hiltke, Tara, Hostetter, Galen, Jędryka, Marcin, Jewell, Scott D., Johnson, Isabelle, Kahn, Andrea G., Ku, Amy T., Kumar-Sinha, Chandan, Kurzawa, Paweł, Lazar, Alexander J., Lazcano, Rossana, Lei, Jonathan T., Li, Yi, Liao, Yuxing, Lih, Tung-Shing M., Lin, Tai-Tu, Martignetti, John A., Masand, Ramya P., Matkowski, Rafał, McKerrow, Wilson, Mesri, Mehdi, Monroe, Matthew E., Moon, Jamie, Moore, Ronald J., Nestor, Michael D., Newton, Chelsea, Omelchenko, Tatiana, Omenn, Gilbert S., Payne, Samuel H., Petyuk, Vladislav A., Robles, Ana I., Rodriguez, Henry, Ruggles, Kelly V., Rykunov, Dmitry, Savage, Sara R., Schepmoes, Athena A., Shi, Tujin, Shi, Zhiao, Tan, Jimin, Taylor, Mason, Thiagarajan, Mathangi, Wang, Joshua M., Weitz, Karl K., Wen, Bo, Williams, C.M., Wu, Yige, Wyczalkowski, Matthew A., Yi, Xinpei, Zhang, Xu, Zhao, Rui, Mutch, David, Chinnaiyan, Arul M., Smith, Richard D., Nesvizhskii, Alexey I., Wang, Pei, Wiznerowicz, Maciej, Ding, Li, Mani, D.R., Zhang, Hui, Anderson, Matthew L., Rodland, Karin D., Zhang, Bing, Liu, Tao, and Fenyö, David

Published
2023
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328. Cluster Analyses From the Real-World NOVELTY Study: Six Clusters Across the Asthma-COPD Spectrum

Author

Olmo, Ricardo del, Anderson, Gary, Reddel, Helen, Rabahi, Marcelo, McIvor, Andrew, Sadatsafavi, Mohsen, Weinreich, Ulla, Burgel, Pierre-Régis, Devouassoux, Gilles, Papi, Alberto, Inoue, Hiromasa, Rendon, Adrián, van den Berge, Maarten, Beasley, Richard, García-Navarro, Alvar Agusti, Faner, Rosa, Olaguibel Rivera, José, Janson, Christer, Bilińska-Izydorczyk, Magdalena, Fagerås, Malin, Fihn-Wikander, Titti, Franzén, Stefan, Keen, Christina, Ostridge, Kristoffer, Chalmers, James, Harrison, Timothy, Pavord, Ian, Price, David, Azim, Adnan, Belton, Laura, Blé, Francois-Xavier, Erhard, Clement, Gairy, Kerry, Hughes, Rod, Lassi, Glenda, Müllerová, Hana, Rapsomaniki, Eleni, Scott, Ian Christopher, Chipps, Bradley, Christenson, Stephanie, Make, Barry, Tomaszewski, Erin, Benhabib, Gabriel, Ruiz, Xavier Bocca, Lisanti, Raul Eduardo, Marino, Gustavo, Mattarucco, Walter, Nogueira, Juan, Parody, Maria, Pascale, Pablo, Rodriguez, Pablo, Silva, Damian, Svetliza, Graciela, Victorio, Carlos F., Rolon, Roxana Willigs, Yañez, Anahi, Baines, Stuart, Bowler, Simon, Bremner, Peter, Bull, Sheetal, Carroll, Patrick, Chaalan, Mariam, Farah, Claude, Hammerschlag, Gary, Hancock, Kerry, Harrington, Zinta, Katsoulotos, Gregory, Kim, Joshua, Langton, David, Lee, Donald, Peters, Matthew, Prassad, Lakshman, Sajkov, Dimitar, Santiago, Francis, Simpson, Frederick Graham, Tai, Sze, Thomas, Paul, Wark, Peter, Cançado, José Eduardo Delfini, Cunha, Thúlio, Lima, Marina, Cardoso, Alexandre Pinto, FitzGerald, J. Mark, Anees, Syed, Bertley, John, Bell, Alan, Cheema, Amarjit, Chouinard, Guy, Csanadi, Michael, Dhar, Anil, Dhillon, Ripple, Kanawaty, David, Kelly, Allan, Killorn, William, Landry, Daniel, Luton, Robert, Mandhane, Piushkumar, Pek, Bonavuth, Petrella, Robert, Stollery, Daniel, Wang, Chen, Chen, Meihua, Chen, Yan, Gu, Wei, Christopher Hui, Kim Ming, Li, Manxiang, Li, Shiyue, Lijun, Ma, Qin, Guangyue, Song, Weidong, Tan, Wei, Tang, Yijun, Wang, Tan, Wen, Fuqiang, Wu, Feng, Xiang, PingChao, Xiao, Zuke, Xiong, Shengdao, Yang, Jinghua, Yang, Jingping, Zhang, Caiqing, Zhang, Min, Zhang, Ping, Zhang, Wei, Zheng, Xiaohe, Zhu, Dan, Bueno, Carlos Matiz, Grimaldos, Fabio Bolivar, Arboleda, Alejandra Cañas, de Salazar, Dora Molina, Bendstrup, Elisabeth, Hilberg, Ole, Kjellerup, Carsten, Raherison, Chantal, Bonniaud, Philippe, Brun, Olivier, Chouaid, Christos, Couturaud, Francis, de Blic, Jacques, Debieuvre, Didier, Delsart, Dominique, Demaegdt, Axelle, Demoly, Pascal, Deschildre, Antoine, Egron, Carole, Falchero, Lionel, Goupil, François, Kessler, Romain, Le Roux, Pascal, Mabire, Pascal, Mahay, Guillaume, Martinez, Stéphanie, Melloni, Boris, Moreau, Laurent, Riviere, Emilie, Roux-Claudé, Pauline, Soulier, Michel, Vignal, Guillaume, Yaici, Azzedine, Bals, Robert, Aries, Sven Philip, Beck, Ekkehard, Deimling, Andreas, Feimer, Jan, Grimm-Sachs, Vera, Groth, Gesine, Herth, Felix, Hoheisel, Gerhard, Kanniess, Frank, Lienert, Thomas, Mronga, Silke, Reinhardt, Jörg, Schlenska, Christian, Stolpe, Christoph, Teber, Ishak, Timmermann, Hartmut, Ulrich, Thomas, Velling, Peter, Wehgartner-Winkler, Sabina, Welling, Juergen, Winkelmann, Ernst-Joachim, Barbetta, Carlo, Braido, Fulvio, Cardaci, Vittorio, Clini, Enrico Maria, Costantino, Maria Teresa, Cuttitta, Giuseppina, di Gioacchino, Mario, Fois, Alessandro, Foschino-Barbaro, Maria Pia, Gammeri, Enrico, Inchingolo, Riccardo, Lavorini, Federico, Molino, Antonio, Nucera, Eleonora, Patella, Vincenzo, Pesci, Alberto, Ricciardolo, Fabio, Rogliani, Paola, Sarzani, Riccardo, Vancheri, Carlo, Vincenti, Rigoletta, Endo, Takeo, Fujita, Masaki, Hara, Yu, Horiguchi, Takahiko, Hosoi, Keita, Ide, Yumiko, Inomata, Minehiko, Inoue, Koji, Inoue, Sumito, Kato, Motokazu, Kawasaki, Masayuki, Kawayama, Tomotaka, Kita, Toshiyuki, Kobayashi, Kanako, Koto, Hiroshi, Nishi, Koichi, Saito, Junpei, Shimizu, Yasuo, Shirai, Toshihiro, Sugihara, Naruhiko, Takahashi, Ken-ichi, Tashimo, Hiroyuki, Tomii, Keisuke, Yamada, Takashi, Yanai, Masaru, Rendon, Adrian, Cerino Javier, Ruth, Domínguez Peregrina, Alfredo, Fernández Corzo, Marco, Montano Gonzalez, Efraín, Ramírez-Venegas, Alejandra, Boersma, Willem, Djamin, R.S., Eijsvogel, Michiel, Franssen, Frits, Goosens, Martijn, Graat-Verboom, Lidwien, Veen, Johannes in 't, Janssen, Rob, Kuppens, Kim, van de Ven, Mario, Bakke, Per, Brunstad, Ole Petter, Einvik, Gunnar, Høines, Kristian Jong, Khusrawi, Alamdar, Oien, Torbjorn, Yoon, Ho Joo, Chang, Yoon-Seok, Cho, Young Joo, Hwang, Yong Il, Kim, Woo Jin, Koh, Young-Il, Lee, Byung-Jae, Lee, Kwan-Ho, Lee, Sang-Pyo, Lee, Yong Chul, Lim, Seong Yong, Min, Kyung Hun, Oh, Yeon-Mok, Park, Choon-Sik, Park, Hae-Sim, Park, Heung-Woo, Rhee, Chin Kook, Yoon, Hyoung-Kyu, García-Navarro, Alvar Agustí, Andújar, Rubén, Anoro, Laura, Buendía García, María, Mozo, Paloma Campo, Campos, Sergio, Casas Maldonado, Francisco, Castilla Martínez, Manuel, Cisneros Serrano, Carolina, Comeche Casanova, Lorena, Corbacho, Dolores, Campo Matías, Felix Del, Echave-Sustaeta, Jose, Corral, Gloria Francisco, Gamboa Setién, Pedro, García Clemente, Marta, Núñez, Ignacio García, García Robaina, Jose, García Salmones, Mercedes, Marín Trigo, Jose Maria, Fernandez, Marta Nuñez, Palomo, Sara Nuñez, Pérez de Llano, Luis, Pueyo Bastida, Ana, Rañó, Ana, Rodríguez González-Moro, José, Reig, Albert Roger, Velasco Garrido, José, Curiac, Dan, Lif-Tiberg, Cornelia, Luts, Anders, Råhlen, Lennart, Rustscheff, Stefan, Adams, Frances, Bradman, Drew, Broughton, Emma, Cosgrove, John, Flood-Page, Patrick, Fuller, Elizabeth, Hartley, David, Hattotuwa, Keith, Jones, Gareth, Lewis, Keir, McGarvey, Lorcan, Morice, Alyn, Pandya, Preeti, Patel, Manish, Roy, Kay, Sathyamurthy, Ramamurthy, Thiagarajan, Swaminathan, Turner, Alice, Vestbo, Jørgen, Wedzicha, Wisia, Wilkinson, Tom, Wilson, Pete, Al-Asadi, Lo’Ay, Anholm, James, Averill, Francis, Bansal, Sandeep, Baptist, Alan, Campbell, Colin, Campos, Michael A., Crook, Gretchen, DeLeon, Samuel, Eid, Alain, Epstein, Ellen, Fritz, Stephen, Harris, Hoadley, Hewitt, Mitzie, Holguin, Fernando, Hudes, Golda, Jackson, Richard, Kaufman, Alan, Kaufman, David, Klapholz, Ari, Krishna, Harshavardhan, Lee, Daria, Lin, Robert, Maselli-Caceres, Diego, Mehta, Vinay, Moy, James N., Nwokoro, Ugo, Parikh, Purvi, Parikh, Sudhir, Perrino, Frank, Ruhlmann, James, Sassoon, Catherine, Settipane, Russell A., Sousa, Daniel, Sriram, Peruvemba, Wachs, Richard, Bansal, Aruna T., Agustí, Alvar, Fageras, Malin, Alacqua, Marianna, and Reddel, Helen K.

Published
2023
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329. Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies

Author

Anirudh, Rushil, Thiagarajan, Jayaraman J., Bremer, Peer-Timo, and Spears, Brian K.

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Physics - Computational Physics, Statistics - Machine Learning
Abstract

Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion. This paper advocates for the training of surrogates that are consistent with the physical manifold -- i.e., predictions are always physically meaningful, and are cyclically consistent -- i.e., when the predictions of the surrogate, when passed through an independently trained inverse model give back the original input parameters. We find that these two consistencies lead to surrogates that are superior in terms of predictive performance, more resilient to sampling artifacts, and tend to be more data efficient. Using Inertial Confinement Fusion (ICF) as a test bed problem, we model a 1D semi-analytic numerical simulator and demonstrate the effectiveness of our approach. Code and data are available at https://github.com/rushilanirudh/macc/, Comment: 10 pages, 6 figures

Published
2019
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330. MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking

Author

Anirudh, Rushil, Thiagarajan, Jayaraman J., Kailkhura, Bhavya, and Bremer, Timo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. As a result, they have been widely adopted across a variety of applications, ranging from challenging inverse problems like image completion, to problems such as anomaly detection and adversarial defense. A recurring theme in many of these applications is the notion of projecting an image observation onto the manifold that is inferred by the generator. In this context, Projected Gradient Descent (PGD) has been the most popular approach, which essentially optimizes for a latent vector that minimizes the discrepancy between a generated image and the given observation. However, PGD is a brittle optimization technique that fails to identify the right projection (or latent vector) when the observation is corrupted, or perturbed even by a small amount. Such corruptions are common in the real world, for example images in the wild come with unknown crops, rotations, missing pixels, or other kinds of non-linear distributional shifts which break current encoding methods, rendering downstream applications unusable. To address this, we propose corruption mimicking -- a new robust projection technique, that utilizes a surrogate network to approximate the unknown corruption directly at test time, without the need for additional supervision or data augmentation. The proposed method is significantly more robust than PGD and other competing methods under a wide variety of corruptions, thereby enabling a more effective use of GANs in real-world applications. More importantly, we show that our approach produces state-of-the-art performance in several GAN-based applications -- anomaly detection, domain adaptation, and adversarial defense, that benefit from an accurate projection., Comment: International Journal on Computer Vision's (IJCV) Special Issue on GANs

Published
2019

331. Enabling Machine Learning-Ready HPC Ensembles with Merlin

Author

Peterson, J. Luc, Bay, Ben, Koning, Joe, Robinson, Peter, Semler, Jessica, White, Jeremy, Anirudh, Rushil, Athey, Kevin, Bremer, Peer-Timo, Di Natale, Francesco, Fox, David, Gaffney, Jim A., Jacobs, Sam A., Kailkhura, Bhavya, Kustowski, Bogdan, Langer, Steven, Spears, Brian, Thiagarajan, Jayaraman, Van Essen, Brian, and Yeom, Jae-Seung

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, Physics - Computational Physics, Physics - Plasma Physics
Abstract

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. In this paper, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. In addition to its design, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19., Comment: 28 pages, 9 figures; Submitted to FGCS

Published
2019

332. Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning

Author

Katoch, Sameeksha, Thopalli, Kowshik, Thiagarajan, Jayaraman J., Turaga, Pavan, and Spanias, Andreas

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Exploiting known semantic relationships between fine-grained tasks is critical to the success of recent model agnostic approaches. These approaches often rely on meta-optimization to make a model robust to systematic task or domain shifts. However, in practice, the performance of these methods can suffer, when there are no coherent semantic relationships between the tasks (or domains). We present Invenio, a structured meta-learning algorithm to infer semantic similarities between a given set of tasks and to provide insights into the complexity of transferring knowledge between different tasks. In contrast to existing techniques such as Task2Vec and Taskonomy, which measure similarities between pre-trained models, our approach employs a novel self-supervised learning strategy to discover these relationships in the training loop and at the same time utilizes them to update task-specific models in the meta-update step. Using challenging task and domain databases, under few-shot learning settings, we show that Invenio can discover intricate dependencies between tasks or domains, and can provide significant gains over existing approaches in terms of generalization performance. The learned semantic structure between tasks/domains from Invenio is interpretable and can be used to construct meaningful priors for tasks or domains., Comment: Semantic structure development for tasks/domains essential for efficient knowledge transfer

Published
2019

333. Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation

Author

An, Eunkyung, Anurag, Meenakshi, Bavarva, Jasmin, Birrer, Michael J., Babur, Özgün, Cao, Song, Ceccarelli, Michele, Chan, Daniel W., Chinnaiyan, Arul M., Cho, Hanbyul, Chowdhury, Shrabanti, Cieslik, Marcin P., Colaprico, Antonio, Carr, Steven A., da Veiga Leprevost, Felipe, Day, Corbin, Domagalski, Marcin J., Dou, Yongchao, Druker, Brian J., Edwards, Nathan, Ellis, Matthew J., Fenyo, David, Foltz, Steven M., Francis, Alicia, Gonzalez Robles, Tania J., Gosline, Sara J.C., Gümüş, Zeynep H., Hiltke, Tara, Hong, Runyu, Hostetter, Galen, Hu, Yingwei, Huang, Chen, Iavarone, Antonio, Jaehnig, Eric J., Jewel, Scott D., Ji, Jiayi, Jiang, Wen, Katsnelson, Lizabeth, Ketchum, Karen A., Kolodziejczak, Iga, Kumar-Sinha, Chandan, Krug, Karsten, Lei, Jonathan T., Liang, Wen-Wei, Liao, Yuxing, Lindgren, Caleb M., Liu, Tao, Liu, Wenke, Ma, Weiping, McKerrow, Wilson, Mesri, Mehdi, Mani, D.R., Nesvizhskii, Alexey I., Newton, Chelsea, Oldroyd, Robert, Omenn, Gilbert S., Paulovich, Amanda G., Petralia, Francesca, Pugliese, Pietro, Reva, Boris, Rodland, Karin D., Ruggles, Kelly V., Rykunov, Dmitry, Rodrigues, Fernanda Martins, Savage, Sara R., Schadt, Eric E., Schnaubelt, Michael, Schraink, Tobias, Shi, Zhiao, Smith, Richard D., Song, Xiaoyu, Stathias, Vasileios, Storrs, Erik P., Schürer, Stephan, Selvan, Myvizhi Esai, Tan, Jimin, Terekhanova, Nadezhda V., Thangudu, Ratna R., Tignor, Nicole, Thiagarajan, Mathangi, Wang, Joshua M., Wang, Pei, Wang, Ying (Cindy), Wen, Bo, Wiznerowicz, Maciej, Wu, Yige, Wyczalkowski, Matthew A., Yao, Lijun, Yi, Xinpei, Zhang, Bing, Zhang, Hui, Zhang, Xu, Zhang, Zhen, Zhou, Daniel Cui, Geffen, Yifat, Anand, Shankara, Akiyama, Yo, Yaron, Tomer M., Song, Yizhe, Johnson, Jared L., Govindan, Akshay, Li, Yize, Huntsman, Emily, Wang, Liang-Bo, Birger, Chet, Heiman, David I., Zhang, Qing, Miller, Mendy, Maruvka, Yosef E., Haradhvala, Nicholas J., Calinawan, Anna, Belkin, Saveliy, Kerelsky, Alexander, Clauser, Karl R., Satpathy, Shankha, Payne, Samuel H., Gillette, Michael A., Dhanasekaran, Saravana M., Rodriguez, Henry, Robles, Ana I., Lazar, Alexander J., Aguet, François, Cantley, Lewis C., Ding, Li, and Getz, Gad

Published
2023
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334. Learn-By-Calibrating: Using Calibration as a Training Objective

Author

Thiagarajan, Jayaraman J., Venkatesh, Bindya, and Rajan, Deepta

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

Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks. In this paper, we argue that such a metric is highly beneficial for training predictive models, even when we do not explicitly measure the uncertainties. This is conceptually similar to heteroscedastic neural networks that produce variance estimates for each prediction, with the key difference that we do not place a Gaussian prior on the predictions. We propose a novel algorithm that performs simultaneous interval estimation for different calibration levels and effectively leverages the intervals to refine the mean estimates. Our results show that, our approach is consistently superior to existing regularization strategies in deep regression models. Finally, we propose to augment partial dependence plots, a model-agnostic interpretability tool, with expected prediction intervals to reveal interesting dependencies between data and the target.

Published
2019

335. Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning

Author

Venkatesh, Bindya and Thiagarajan, Jayaraman J.

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

The role of uncertainty quantification (UQ) in deep learning has become crucial with growing use of predictive models in high-risk applications. Though a large class of methods exists for measuring deep uncertainties, in practice, the resulting estimates are found to be poorly calibrated, thus making it challenging to translate them into actionable insights. A common workaround is to utilize a separate recalibration step, which adjusts the estimates to compensate for the miscalibration. Instead, we propose to repurpose the heteroscedastic regression objective as a surrogate for calibration and enable any existing uncertainty estimator to be inherently calibrated. In addition to eliminating the need for recalibration, this also regularizes the training process. Using regression experiments, we demonstrate the effectiveness of the proposed heteroscedastic calibration with two popular uncertainty estimators.

Published
2019

336. Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

Author

Anirudh, Rushil, Thiagarajan, Jayaraman J., Liu, Shusen, Bremer, Peer-Timo, and Spears, Brian K.

Subjects
Physics - Computational Physics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion. However, a common challenge is to verify the scientific plausibility or validity of outputs predicted by a neural network. This work advocates the use of known scientific constraints as a lens into evaluating, exploring, and understanding such predictions for the problem of inertial confinement fusion., Comment: Machine Learning for Physical Sciences Workshop at NeurIPS 2019

Published
2019

337. Improving Limited Angle CT Reconstruction with a Robust GAN Prior

Author

Anirudh, Rushil, Kim, Hyojin, Thiagarajan, Jayaraman J., Mohan, K. Aditya, and Champley, Kyle M.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved. In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by effectively projecting onto the inferred image manifold. In particular, we use a robust version of the popularly used GAN prior for inverse problems, based on a recent technique called corruption mimicking, that significantly improves the reconstruction quality. The proposed approach operates in the image space directly, as a result of which it does not need to be trained or require access to the measurement model, is scanner agnostic, and can work over a wide range of sensing scenarios., Comment: NeurIPS 2019 Workshop on Deep Inverse Problems

Published
2019

338. Proteogenomic data and resources for pan-cancer analysis

Author

Lazar, Alexander J., Paulovich, Amanda G., Colaprico, Antonio, Iavarone, Antonio, Chinnaiyan, Arul M., Druker, Brian J., Kumar-Sinha, Chandan, Newton, Chelsea J., Huang, Chen, Mani, D.R., Smith, Richard D., Huntsman, Emily, Schadt, Eric E., An, Eunkyung, Petralia, Francesca, Hostetter, Galen, Omenn, Gilbert S., Cho, Hanbyul, Rodriguez, Henry, Zhang, Hui, Kolodziejczak, Iga, Johnson, Jared L., Bavarva, Jasmin, Tan, Jimin, Rodland, Karin D., Clauser, Karl R., Krug, Karsten, Cantley, Lewis C., Wiznerowicz, Maciej, Ellis, Matthew J., Anurag, Meenakshi, Mesri, Mehdi, Gillette, Michael A., Birrer, Michael J., Ceccarelli, Michele, Dhanasekaran, Saravana M., Edwards, Nathan, Tignor, Nicole, Babur, Özgün, Pugliese, Pietro, Gosline, Sara J.C., Jewell, Scott D., Satpathy, Shankha, Chowdhury, Shrabanti, Schürer, Stephan, Carr, Steven A., Liu, Tao, Hiltke, Tara, Yaron, Tomer M., Stathias, Vasileios, Liu, Wenke, Zhang, Xu, Song, Yizhe, Zhang, Zhen, Chan, Daniel W., Li, Yize, Dou, Yongchao, Da Veiga Leprevost, Felipe, Geffen, Yifat, Calinawan, Anna P., Aguet, François, Akiyama, Yo, Anand, Shankara, Birger, Chet, Cao, Song, Chaudhary, Rekha, Chilappagari, Padmini, Cieslik, Marcin, Zhou, Daniel Cui, Day, Corbin, Domagalski, Marcin J., Esai Selvan, Myvizhi, Fenyö, David, Foltz, Steven M., Francis, Alicia, Gonzalez-Robles, Tania, Gümüş, Zeynep H., Heiman, David, Holck, Michael, Hong, Runyu, Hu, Yingwei, Jaehnig, Eric J., Ji, Jiayi, Jiang, Wen, Katsnelson, Lizabeth, Ketchum, Karen A., Klein, Robert J., Lei, Jonathan T., Liang, Wen-Wei, Liao, Yuxing, Lindgren, Caleb M., Ma, Weiping, Ma, Lei, MacCoss, Michael J., Martins Rodrigues, Fernanda, McKerrow, Wilson, Nguyen, Ngoc, Oldroyd, Robert, Pilozzi, Alexander, Reva, Boris, Rudnick, Paul, Ruggles, Kelly V., Rykunov, Dmitry, Savage, Sara R., Schnaubelt, Michael, Schraink, Tobias, Shi, Zhiao, Singhal, Deepak, Song, Xiaoyu, Storrs, Erik, Terekhanova, Nadezhda V., Thangudu, Ratna R., Thiagarajan, Mathangi, Wang, Liang-Bo, Wang, Joshua M., Wang, Ying, Wen, Bo, Wu, Yige, Wyczalkowski, Matthew A., Xin, Yi, Yao, Lijun, Yi, Xinpei, Zhang, Qing, Zuhl, Maya, Getz, Gad, Ding, Li, Nesvizhskii, Alexey I., Wang, Pei, Robles, Ana I., Zhang, Bing, and Payne, Samuel H.

Published
2023
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339. Function Preserving Projection for Scalable Exploration of High-Dimensional Data

Author

Liu, Shusen, Anirudh, Rushil, Thiagarajan, Jayaraman J., and Bremer, Peer-Timo

Subjects
Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimension reduction methods aim to maximally preserve the global and/or local geometric structure of a dataset. However, in practice one is often more interested in determining how one or multiple user-selected response function(s) can be explained by the data. To intuitively connect the responses to the data, FPP constructs 2D linear embeddings optimized to reveal interpretable yet potentially non-linear patterns of the response functions. More specifically, FPP is designed to (i) produce human-interpretable embeddings; (ii) capture non-linear relationships; (iii) allow the simultaneous use of multiple response functions; and (iv) scale to millions of samples. Using FPP on real-world datasets, one can obtain fundamentally new insights about high-dimensional relationships in large-scale data that could not be achieved using existing dimension reduction methods.

Published
2019

340. Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

Author

Thiagarajan, Jayaraman J., Venkatesh, Bindya, Sattigeri, Prasanna, and Bremer, Peer-Timo

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

With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties. While identifying all sources that account for the stochasticity of models is challenging, it is common to augment predictions with confidence intervals to convey the expected variations in a model's behavior. We require prediction intervals to be well-calibrated, reflect the true uncertainties, and to be sharp. However, existing techniques for obtaining prediction intervals are known to produce unsatisfactory results in at least one of these criteria. To address this challenge, we develop a novel approach for building calibrated estimators. More specifically, we use separate models for prediction and interval estimation, and pose a bi-level optimization problem that allows the former to leverage estimates from the latter through an \textit{uncertainty matching} strategy. Using experiments in regression, time-series forecasting, and object localization, we show that our approach achieves significant improvements over existing uncertainty quantification methods, both in terms of model fidelity and calibration error., Comment: AAAI 2020

Published
2019

341. Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation

Author

Thiagarajan, Jayaraman J., Kashyap, Satyananda, and Karagyris, Alexandros

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis. In contrast to conventional instance segmentation scenarios in computer vision, the problems that we consider are characterized by a small number of training images and non-local patterns that lead to the diagnosis. In this paper, we explore the use of multiple instance learning (MIL) to design an instance label generator under this weakly supervised setting. Motivated by the observation that an MIL model can handle bags of varying sizes, we propose to repurpose an MIL model originally trained for bag-level classification to produce reliable predictions for single instances, i.e., bags of size $1$. To this end, we introduce a novel regularization strategy based on virtual adversarial training for improving MIL training, and subsequently develop a knowledge distillation technique for repurposing the trained MIL model. Using empirical studies on colon cancer and breast cancer detection from histopathological images, we show that the proposed approach produces high-quality instance-level prediction and significantly outperforms state-of-the MIL methods.

Published
2019

342. Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

Author

Liu, Shusen, Wang, Di, Maljovec, Dan, Anirudh, Rushil, Thiagarajan, Jayaraman J., Jacobs, Sam Ade, Van Essen, Brian C., Hysom, David, Yeom, Jae-Seung, Gaffney, Jim, Peterson, Luc, Robinson, Peter B., Bhatia, Harsh, Pascucci, Valerio, Spears, Brian K., and Bremer, Peer-Timo

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Computer Science - Neural and Evolutionary Computing, Statistics - Machine Learning
Abstract

With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.

Published
2019

343. SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation

Author

Thopalli, Kowshik, Thiagarajan, Jayaraman J., Anirudh, Rushil, and Turaga, Pavan

Subjects
Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Unsupervised domain adaptation aims to transfer and adapt knowledge learned from a labeled source domain to an unlabeled target domain. Key components of unsupervised domain adaptation include: (a) maximizing performance on the target, and (b) aligning the source and target domains. Traditionally, these tasks have either been considered as separate, or assumed to be implicitly addressed together with high-capacity feature extractors. When considered separately, alignment is usually viewed as a problem of aligning data distributions, either through geometric approaches such as subspace alignment or through distributional alignment such as optimal transport. This paper represents a hybrid approach, where we assume simplified data geometry in the form of subspaces, and consider alignment as an auxiliary task to the primary task of maximizing performance on the source. The alignment is made rather simple by leveraging tractable data geometry in the form of subspaces. We synergistically allow certain parameters derived from the closed-form auxiliary solution, to be affected by gradients from the primary task. The proposed approach represents a unique fusion of geometric and model-based alignment with gradients from a data-driven primary task. Our approach termed SALT, is a simple framework that achieves comparable or sometimes outperforms state-of-the-art on multiple standard benchmarks.

Published
2019

344. A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis

Author

Kailkhura, Bhavya, Thiagarajan, Jayaraman J., Li, Qunwei, and Bremer, Peer-Timo

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

This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, expressed in terms of the power spectra of the sampling pattern and the function involved. The framework is build in the Euclidean space using Fourier analysis and establishes a connection between some high dimensional geometric objects and optimal spectral form of different state-of-the-art sampling patterns. Subsequently, we estimate the expected error bounds and convergence rate of different state-of-the-art sampling patterns, as the number of samples and dimensions increase. We make several observations about generalization error which are valid irrespective of the approximation scheme (or learning architecture) and training (or optimization) algorithms. Our result also sheds light on ways to formulate design principles for constructing optimal sampling methods for particular problems.

Published
2019

346. The Sub-band Structure of Atomically Sharp Dopant Profiles in Silicon

Author

Mazzola, Federico, Chen, Chin-Yi, Rahman, Rajib, Zhu, Xie-Gang, Polley, Craig M., Balasubramanian, Thiagarajan, King, Phil D. C., Hofmann, Philip, Miwa, Jill A., and Wells, Justin W.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract

The downscaling of silicon-based structures and proto-devices has now reached the single atom scale, representing an important milestone for the development of a silicon-based quantum computer. One especially notable platform for atomic scale device fabrication is the so-called SiP delta-layer, consisting of an ultra dense and sharp layer of dopants within a semiconductor host. Whilst several alternatives exist, phosphorus dopants in silicon have drawn the most interest, and it is on this platform that many quantum proto-devices have been successfully demonstrated. Motivated by this, both calculations and experiments have been dedicated to understanding the electronic structure of the SiP delta-layer platform. In this work, we use high resolution angle-resolved photoemission spectroscopy (ARPES) to reveal the structure of the electronic states which exist because of the high dopant density of the SiP delta-layer. In contrast to published theoretical work, we resolve three distinct bands, the most occupied of which shows a large anisotropy and significant deviation from simple parabolic behaviour. We investigate the possible origins of this fine structure, and conclude that it is primarily a consequence of the dielectric constant being large (ca. double that of bulk Si). Incorporating this factor into tight binding calculations leads to a major revision of band structure; specifically, the existence of a third band, the separation of the bands, and the departure from purely parabolic behaviour. This new understanding of the bandstructure has important implications for quantum proto-devices which are built on the SiP delta-layer platform., Comment: 6 pages, 3 figures

Published
2019

347. Distribution System State Estimation in the Presence of High Solar Penetration

Author

Ramachandran, Thiagarajan, Reiman, Andrew, Nandanoori, Sai Pushpak, Rice, Mark, and Kundu, Soumya

Subjects
Computer Science - Systems and Control, Mathematics - Optimization and Control
Abstract

Low-to-medium voltage distribution networks are experiencing rising levels of distributed energy resources, including renewable generation, along with improved sensing, communication, and automation infrastructure. As such, state estimation methods for distribution systems are becoming increasingly relevant as a means to enable better control strategies that can both leverage the benefits and mitigate the risks associated with high penetration of variable and uncertain distributed generation resources. The primary challenges of this problem include modeling complexities (nonlinear, non-convex power-flow equations), limited availability of sensor measurements, and high penetration of uncertain renewable generation. This paper formulates the distribution system state estimation as a nonlinear, weighted, least squares problem, based on sensor measurements as well as forecast data (both load and generation). We investigate the sensitivity of state estimator accuracy to (load/generation) forecast uncertainties, sensor accuracy, and sensor coverage levels., Comment: accepted for presentation at the IEEE 2019 American Control Conference

Published
2019

348. Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets

Author

Narayanaswamy, Vivek Sivaraman, Katoch, Sameeksha, Thiagarajan, Jayaraman J., Song, Huan, and Spanias, Andreas

Subjects
Computer Science - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Statistics - Machine Learning
Abstract

Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specifically, recent focus is on time-domain based architectures such as Wave-U-Net which exploit temporal context by extracting multi-scale features. However, the optimality of the feature extraction process in these architectures has not been well investigated. In this paper, we examine and recommend critical architectural changes that forge an optimal multi-scale feature extraction process. To this end, we replace regular $1-$D convolutions with adaptive dilated convolutions that have innate capability of capturing increased context by using large temporal receptive fields. We also investigate the impact of dense connections on the extraction process that encourage feature reuse and better gradient flow. The dense connections between the downsampling and upsampling paths of a U-Net architecture capture multi-resolution information leading to improved temporal modelling. We evaluate the proposed approaches on the MUSDB test dataset. In addition to providing an improved performance over the state-of-the-art, we also provide insights on the impact of different architectural choices on complex data-driven solutions for source separation.

Published
2019

349. Identification and Validation of Virtual Battery Model for Heterogeneous Devices

Author

Nandanoori, Sai Pushpak, Chakraborty, Indrasis, Ramachandran, Thiagarajan, and Kundu, Soumya

Subjects
Mathematics - Optimization and Control, Computer Science - Systems and Control
Abstract

The potential of distributed energy resources in providing grid services can be maximized with the recent advancements in demand side control. Effective utilization of this control strategy requires the knowledge of aggregate flexibility of the distributed energy resources (DERs). Recent works have shown that the aggregate flexibility of DERs can be modeled as a virtual battery (VB) whose state evolution is governed by a first order system including self-dissipation. The VB parameters (self-dissipation rate, energy capacity) are obtained by solving an optimization problem which minimizes the tracking performance of the ensemble and the proposed first-order model. For the identified first order model, time-varying power limits are calculated using binary search algorithms. Finally, this proposed framework is demonstrated for different homogeneous and heterogeneous ensembles consisting of air conditioners (ACs) and electric water heaters (EWHs)., Comment: This is a preprint version of PESGM 2019 (accepted) paper

Published
2019

350. Polycomb group genes are required for neuronal pruning in Drosophila

Author

Shufeng Bu, Samuel Song Yuan Lau, Wei Lin Yong, Heng Zhang, Sasinthiran Thiagarajan, Arash Bashirullah, and Fengwei Yu

Subjects
Polycomb group genes, Hox genes, Dendrite pruning, Axon pruning, Ecdysone signalling, Metamorphosis, Biology (General), QH301-705.5
Abstract

Abstract Background Pruning that selectively eliminates unnecessary or incorrect neurites is required for proper wiring of the mature nervous system. During Drosophila metamorphosis, dendritic arbourization sensory neurons (ddaCs) and mushroom body (MB) γ neurons can selectively prune their larval dendrites and/or axons in response to the steroid hormone ecdysone. An ecdysone-induced transcriptional cascade plays a key role in initiating neuronal pruning. However, how downstream components of ecdysone signalling are induced remains not entirely understood. Results Here, we identify that Scm, a component of Polycomb group (PcG) complexes, is required for dendrite pruning of ddaC neurons. We show that two PcG complexes, PRC1 and PRC2, are important for dendrite pruning. Interestingly, depletion of PRC1 strongly enhances ectopic expression of Abdominal B (Abd-B) and Sex combs reduced, whereas loss of PRC2 causes mild upregulation of Ultrabithorax and Abdominal A in ddaC neurons. Among these Hox genes, overexpression of Abd-B causes the most severe pruning defects, suggesting its dominant effect. Knockdown of the core PRC1 component Polyhomeotic (Ph) or Abd-B overexpression selectively downregulates Mical expression, thereby inhibiting ecdysone signalling. Finally, Ph is also required for axon pruning and Abd-B silencing in MB γ neurons, indicating a conserved function of PRC1 in two types of pruning. Conclusions This study demonstrates important roles of PcG and Hox genes in regulating ecdysone signalling and neuronal pruning in Drosophila. Moreover, our findings suggest a non-canonical and PRC2-independent role of PRC1 in Hox gene silencing during neuronal pruning.

Published
2023
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