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1. Leukocytosis and thrombocytosis after splenectomy: expected finding, infection, or something else: a case report.

Author: Gonzalez, Nicolas, Nahmias, Jeffry, Lee, Lisa, Dolich, Matthew, Lekawa, Michael, Kong, Allen, and Grigorian, Areg
Subjects: Case report, Chronic myeloid leukemia, Leukocytosis, Splenectomy, Thrombocytosis, Trauma, Humans, Male, Splenectomy, Middle Aged, Thrombocytosis, Leukocytosis, Pancreatectomy, Tomography, X-Ray Computed, Postoperative Complications, Imatinib Mesylate, Wounds, Nonpenetrating
Abstract: BACKGROUND: Leukocytosis and thrombocytosis often follow splenectomy in blunt trauma patients, complicating the postoperative identification of infection. While the platelet count to white blood cell ratio provides diagnostic assistance to discern between expected laboratory alterations and infection, diagnoses such as leukemia are often overlooked. CASE PRESENTATION: A 53-year-old Hispanic male presented with abdominal pain, nausea, tachycardia, and focal peritonitis 4 days after being assaulted and struck multiple times in the abdomen. Initial white blood cell count was 38.4 × 109/L, platelet count was 691 × 109/L, and lipase was 55 U/L. Computed tomography abdomen/pelvis demonstrated a hematoma encasing the distal pancreas and abutting the stomach and colon. Emergent laparotomy revealed a nearly transected pancreas and devascularized colon, necessitating a distal pancreatectomy, splenectomy, and colonic resection with primary anastomosis. Postoperatively, he had a persistently elevated leukocytosis, thrombocytosis, segmented neutrophils, eosinophilia, and basophilia (peak at 70, 2293, 64, 1.1, and 1.2 × 109/L, respectively). Despite sepsis workup, including repeat computed tomography, no source was identified. Hematology/oncology was consulted for concern for hematologic etiology, with genetic testing and bone marrow biopsy performed. The diagnosis of breakpoint cluster-Abelson gene-positive chronic myeloid leukemia was made based on genetic tests, including polymerase chain reaction and fluorescence in situ hybridization analysis, which confirmed the presence of the Philadelphia chromosome. Bone marrow biopsy suggested a chronic phase. The patient was treated with hydroxyurea and transitioned to imatinib. CONCLUSIONS: Thrombocytosis following splenectomy is a common complication and a plate count to white blood cell count ratio 50 × 109/L) and thrombocytosis (> 2000 × 109/L) may suggest something more ominous, including chronic myeloid leukemia , particularly when elevated granulocyte counts are present. Chronic myeloid leukemia workup includes peripheral smear, bone marrow aspiration, and determination of Philadelphia chromosome. Post-splenectomy vaccines are still indicated within 14 days; however, the timing of immunization with cancer treatment must be considered. Tyrosine kinase inhibitors are the first-line therapy and benefits of pretreatment with hydroxyurea for cytoreduction remain under investigation. Additionally, tyrosine kinase inhibitors have been associated with gastrointestinal perforation and impaired wound healing, necessitating heightened attention in patients with a new bowel anastomosis.
Published: 2024

2. Equitable Health Care and Low-Density Living in the United States

Author: Lee, Lisa M
Published: 2019
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3. Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial

Author: Illingworth, Peter J., Venetis, Christos, Gardner, David K., Nelson, Scott M., Berntsen, Jørgen, Larman, Mark G., Agresta, Franca, Ahitan, Saran, Ahlström, Aisling, Cattrall, Fleur, Cooke, Simon, Demmers, Kristy, Gabrielsen, Anette, Hindkjær, Johnny, Kelley, Rebecca L., Knight, Charlotte, Lee, Lisa, Lahoud, Robert, Mangat, Manveen, Park, Hannah, Price, Anthony, Trew, Geoffrey, Troest, Bettina, Vincent, Anna, Wennerström, Susanne, Zujovic, Lyndsey, and Hardarson, Thorir
Published: 2024
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4. The Stories We Collect: Promoting Housing as a Human Right at the National Public Housing Museum

Author: Lee, Lisa Yun
Published: 2017

5. Gemma: Open Models Based on Gemini Research and Technology

Author: Gemma Team, Mesnard, Thomas, Hardin, Cassidy, Dadashi, Robert, Bhupatiraju, Surya, Pathak, Shreya, Sifre, Laurent, Rivière, Morgane, Kale, Mihir Sanjay, Love, Juliette, Tafti, Pouya, Hussenot, Léonard, Sessa, Pier Giuseppe, Chowdhery, Aakanksha, Roberts, Adam, Barua, Aditya, Botev, Alex, Castro-Ros, Alex, Slone, Ambrose, Héliou, Amélie, Tacchetti, Andrea, Bulanova, Anna, Paterson, Antonia, Tsai, Beth, Shahriari, Bobak, Lan, Charline Le, Choquette-Choo, Christopher A., Crepy, Clément, Cer, Daniel, Ippolito, Daphne, Reid, David, Buchatskaya, Elena, Ni, Eric, Noland, Eric, Yan, Geng, Tucker, George, Muraru, George-Christian, Rozhdestvenskiy, Grigory, Michalewski, Henryk, Tenney, Ian, Grishchenko, Ivan, Austin, Jacob, Keeling, James, Labanowski, Jane, Lespiau, Jean-Baptiste, Stanway, Jeff, Brennan, Jenny, Chen, Jeremy, Ferret, Johan, Chiu, Justin, Mao-Jones, Justin, Lee, Katherine, Yu, Kathy, Millican, Katie, Sjoesund, Lars Lowe, Lee, Lisa, Dixon, Lucas, Reid, Machel, Mikuła, Maciej, Wirth, Mateo, Sharman, Michael, Chinaev, Nikolai, Thain, Nithum, Bachem, Olivier, Chang, Oscar, Wahltinez, Oscar, Bailey, Paige, Michel, Paul, Yotov, Petko, Chaabouni, Rahma, Comanescu, Ramona, Jana, Reena, Anil, Rohan, McIlroy, Ross, Liu, Ruibo, Mullins, Ryan, Smith, Samuel L, Borgeaud, Sebastian, Girgin, Sertan, Douglas, Sholto, Pandya, Shree, Shakeri, Siamak, De, Soham, Klimenko, Ted, Hennigan, Tom, Feinberg, Vlad, Stokowiec, Wojciech, Chen, Yu-hui, Ahmed, Zafarali, Gong, Zhitao, Warkentin, Tris, Peran, Ludovic, Giang, Minh, Farabet, Clément, Vinyals, Oriol, Dean, Jeff, Kavukcuoglu, Koray, Hassabis, Demis, Ghahramani, Zoubin, Eck, Douglas, Barral, Joelle, Pereira, Fernando, Collins, Eli, Joulin, Armand, Fiedel, Noah, Senter, Evan, Andreev, Alek, and Kenealy, Kathleen
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations.
Published: 2024

6. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author: Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.
Published: 2024

7. Synergistic disparities and public health mitigation of COVID-19 in the rural United States

Author: Chillag, Kata and Lee, Lisa M.
Published: 2020

8. Gemini: A Family of Highly Capable Multimodal Models

Author: Gemini Team, Anil, Rohan, Borgeaud, Sebastian, Alayrac, Jean-Baptiste, Yu, Jiahui, Soricut, Radu, Schalkwyk, Johan, Dai, Andrew M., Hauth, Anja, Millican, Katie, Silver, David, Johnson, Melvin, Antonoglou, Ioannis, Schrittwieser, Julian, Glaese, Amelia, Chen, Jilin, Pitler, Emily, Lillicrap, Timothy, Lazaridou, Angeliki, Firat, Orhan, Molloy, James, Isard, Michael, Barham, Paul R., Hennigan, Tom, Lee, Benjamin, Viola, Fabio, Reynolds, Malcolm, Xu, Yuanzhong, Doherty, Ryan, Collins, Eli, Meyer, Clemens, Rutherford, Eliza, Moreira, Erica, Ayoub, Kareem, Goel, Megha, Krawczyk, Jack, Du, Cosmo, Chi, Ed, Cheng, Heng-Tze, Ni, Eric, Shah, Purvi, Kane, Patrick, Chan, Betty, Faruqui, Manaal, Severyn, Aliaksei, Lin, Hanzhao, Li, YaGuang, Cheng, Yong, Ittycheriah, Abe, Mahdieh, Mahdis, Chen, Mia, Sun, Pei, Tran, Dustin, Bagri, Sumit, Lakshminarayanan, Balaji, Liu, Jeremiah, Orban, Andras, Güra, Fabian, Zhou, Hao, Song, Xinying, Boffy, Aurelien, Ganapathy, Harish, Zheng, Steven, Choe, HyunJeong, Weisz, Ágoston, Zhu, Tao, Lu, Yifeng, Gopal, Siddharth, Kahn, Jarrod, Kula, Maciej, Pitman, Jeff, Shah, Rushin, Taropa, Emanuel, Merey, Majd Al, Baeuml, Martin, Chen, Zhifeng, Shafey, Laurent El, Zhang, Yujing, Sercinoglu, Olcan, Tucker, George, Piqueras, Enrique, Krikun, Maxim, Barr, Iain, Savinov, Nikolay, Danihelka, Ivo, Roelofs, Becca, White, Anaïs, Andreassen, Anders, von Glehn, Tamara, Yagati, Lakshman, Kazemi, Mehran, Gonzalez, Lucas, Khalman, Misha, Sygnowski, Jakub, Frechette, Alexandre, Smith, Charlotte, Culp, Laura, Proleev, Lev, Luan, Yi, Chen, Xi, Lottes, James, Schucher, Nathan, Lebron, Federico, Rrustemi, Alban, Clay, Natalie, Crone, Phil, Kocisky, Tomas, Zhao, Jeffrey, Perz, Bartek, Yu, Dian, Howard, Heidi, Bloniarz, Adam, Rae, Jack W., Lu, Han, Sifre, Laurent, Maggioni, Marcello, Alcober, Fred, Garrette, Dan, Barnes, Megan, Thakoor, Shantanu, Austin, Jacob, Barth-Maron, Gabriel, Wong, William, Joshi, Rishabh, Chaabouni, Rahma, Fatiha, Deeni, Ahuja, Arun, Tomar, Gaurav Singh, Senter, Evan, Chadwick, Martin, Kornakov, Ilya, Attaluri, Nithya, Iturrate, Iñaki, Liu, Ruibo, Li, Yunxuan, Cogan, Sarah, Chen, Jeremy, Jia, Chao, Gu, Chenjie, Zhang, Qiao, Grimstad, Jordan, Hartman, Ale Jakse, Garcia, Xavier, Pillai, Thanumalayan Sankaranarayana, Devlin, Jacob, Laskin, Michael, Casas, Diego de Las, Valter, Dasha, Tao, Connie, Blanco, Lorenzo, Badia, Adrià Puigdomènech, Reitter, David, Chen, Mianna, Brennan, Jenny, Rivera, Clara, Brin, Sergey, Iqbal, Shariq, Surita, Gabriela, Labanowski, Jane, Rao, Abhi, Winkler, Stephanie, Parisotto, Emilio, Gu, Yiming, Olszewska, Kate, Addanki, Ravi, Miech, Antoine, Louis, Annie, Teplyashin, Denis, Brown, Geoff, Catt, Elliot, Balaguer, Jan, Xiang, Jackie, Wang, Pidong, Ashwood, Zoe, Briukhov, Anton, Webson, Albert, Ganapathy, Sanjay, Sanghavi, Smit, Kannan, Ajay, Chang, Ming-Wei, Stjerngren, Axel, Djolonga, Josip, Sun, Yuting, Bapna, Ankur, Aitchison, Matthew, Pejman, Pedram, Michalewski, Henryk, Yu, Tianhe, Wang, Cindy, Love, Juliette, Ahn, Junwhan, Bloxwich, Dawn, Han, Kehang, Humphreys, Peter, Sellam, Thibault, Bradbury, James, Godbole, Varun, Samangooei, Sina, Damoc, Bogdan, Kaskasoli, Alex, Arnold, Sébastien M. R., Vasudevan, Vijay, Agrawal, Shubham, Riesa, Jason, Lepikhin, Dmitry, Tanburn, Richard, Srinivasan, Srivatsan, Lim, Hyeontaek, Hodkinson, Sarah, Shyam, Pranav, Ferret, Johan, Hand, Steven, Garg, Ankush, Paine, Tom Le, Li, Jian, Li, Yujia, Giang, Minh, Neitz, Alexander, Abbas, Zaheer, York, Sarah, Reid, Machel, Cole, Elizabeth, Chowdhery, Aakanksha, Das, Dipanjan, Rogozińska, Dominika, Nikolaev, Vitaliy, Sprechmann, Pablo, Nado, Zachary, Zilka, Lukas, Prost, Flavien, He, Luheng, Monteiro, Marianne, Mishra, Gaurav, Welty, Chris, Newlan, Josh, Jia, Dawei, Allamanis, Miltiadis, Hu, Clara Huiyi, de Liedekerke, Raoul, Gilmer, Justin, Saroufim, Carl, Rijhwani, Shruti, Hou, Shaobo, Shrivastava, Disha, Baddepudi, Anirudh, Goldin, Alex, Ozturel, Adnan, Cassirer, Albin, Xu, Yunhan, Sohn, Daniel, Sachan, Devendra, Amplayo, Reinald Kim, Swanson, Craig, Petrova, Dessie, Narayan, Shashi, Guez, Arthur, Brahma, Siddhartha, Landon, Jessica, Patel, Miteyan, Zhao, Ruizhe, Villela, Kevin, Wang, Luyu, Jia, Wenhao, Rahtz, Matthew, Giménez, Mai, Yeung, Legg, Keeling, James, Georgiev, Petko, Mincu, Diana, Wu, Boxi, Haykal, Salem, Saputro, Rachel, Vodrahalli, Kiran, Qin, James, Cankara, Zeynep, Sharma, Abhanshu, Fernando, Nick, Hawkins, Will, Neyshabur, Behnam, Kim, Solomon, Hutter, Adrian, Agrawal, Priyanka, Castro-Ros, Alex, Driessche, George van den, Wang, Tao, Yang, Fan, Chang, Shuo-yiin, Komarek, Paul, McIlroy, Ross, Lučić, Mario, Zhang, Guodong, Farhan, Wael, Sharman, Michael, Natsev, Paul, Michel, Paul, Bansal, Yamini, Qiao, Siyuan, Cao, Kris, Shakeri, Siamak, Butterfield, Christina, Chung, Justin, Rubenstein, Paul Kishan, Agrawal, Shivani, Mensch, Arthur, Soparkar, Kedar, Lenc, Karel, Chung, Timothy, Pope, Aedan, Maggiore, Loren, Kay, Jackie, Jhakra, Priya, Wang, Shibo, Maynez, Joshua, Phuong, Mary, Tobin, Taylor, Tacchetti, Andrea, Trebacz, Maja, Robinson, Kevin, Katariya, Yash, Riedel, Sebastian, Bailey, Paige, Xiao, Kefan, Ghelani, Nimesh, Aroyo, Lora, Slone, Ambrose, Houlsby, Neil, Xiong, Xuehan, Yang, Zhen, Gribovskaya, Elena, Adler, Jonas, Wirth, Mateo, Lee, Lisa, Li, Music, Kagohara, Thais, Pavagadhi, Jay, Bridgers, Sophie, Bortsova, Anna, Ghemawat, Sanjay, Ahmed, Zafarali, Liu, Tianqi, Powell, Richard, Bolina, Vijay, Iinuma, Mariko, Zablotskaia, Polina, Besley, James, Chung, Da-Woon, Dozat, Timothy, Comanescu, Ramona, Si, Xiance, Greer, Jeremy, Su, Guolong, Polacek, Martin, Kaufman, Raphaël Lopez, Tokumine, Simon, Hu, Hexiang, Buchatskaya, Elena, Miao, Yingjie, Elhawaty, Mohamed, Siddhant, Aditya, Tomasev, Nenad, Xing, Jinwei, Greer, Christina, Miller, Helen, Ashraf, Shereen, Roy, Aurko, Zhang, Zizhao, Ma, Ada, Filos, Angelos, Besta, Milos, Blevins, Rory, Klimenko, Ted, Yeh, Chih-Kuan, Changpinyo, Soravit, Mu, Jiaqi, Chang, Oscar, Pajarskas, Mantas, Muir, Carrie, Cohen, Vered, Lan, Charline Le, Haridasan, Krishna, Marathe, Amit, Hansen, Steven, Douglas, Sholto, Samuel, Rajkumar, Wang, Mingqiu, Austin, Sophia, Lan, Chang, Jiang, Jiepu, Chiu, Justin, Lorenzo, Jaime Alonso, Sjösund, Lars Lowe, Cevey, Sébastien, Gleicher, Zach, Avrahami, Thi, Boral, Anudhyan, Srinivasan, Hansa, Selo, Vittorio, May, Rhys, Aisopos, Konstantinos, Hussenot, Léonard, Soares, Livio Baldini, Baumli, Kate, Chang, Michael B., Recasens, Adrià, Caine, Ben, Pritzel, Alexander, Pavetic, Filip, Pardo, Fabio, Gergely, Anita, Frye, Justin, Ramasesh, Vinay, Horgan, Dan, Badola, Kartikeya, Kassner, Nora, Roy, Subhrajit, Dyer, Ethan, Campos, Víctor Campos, Tomala, Alex, Tang, Yunhao, Badawy, Dalia El, White, Elspeth, Mustafa, Basil, Lang, Oran, Jindal, Abhishek, Vikram, Sharad, Gong, Zhitao, Caelles, Sergi, Hemsley, Ross, Thornton, Gregory, Feng, Fangxiaoyu, Stokowiec, Wojciech, Zheng, Ce, Thacker, Phoebe, Ünlü, Çağlar, Zhang, Zhishuai, Saleh, Mohammad, Svensson, James, Bileschi, Max, Patil, Piyush, Anand, Ankesh, Ring, Roman, Tsihlas, Katerina, Vezer, Arpi, Selvi, Marco, Shevlane, Toby, Rodriguez, Mikel, Kwiatkowski, Tom, Daruki, Samira, Rong, Keran, Dafoe, Allan, FitzGerald, Nicholas, Gu-Lemberg, Keren, Khan, Mina, Hendricks, Lisa Anne, Pellat, Marie, Feinberg, Vladimir, Cobon-Kerr, James, Sainath, Tara, Rauh, Maribeth, Hashemi, Sayed Hadi, Ives, Richard, Hasson, Yana, Noland, Eric, Cao, Yuan, Byrd, Nathan, Hou, Le, Wang, Qingze, Sottiaux, Thibault, Paganini, Michela, Lespiau, Jean-Baptiste, Moufarek, Alexandre, Hassan, Samer, Shivakumar, Kaushik, van Amersfoort, Joost, Mandhane, Amol, Joshi, Pratik, Goyal, Anirudh, Tung, Matthew, Brock, Andrew, Sheahan, Hannah, Misra, Vedant, Li, Cheng, Rakićević, Nemanja, Dehghani, Mostafa, Liu, Fangyu, Mittal, Sid, Oh, Junhyuk, Noury, Seb, Sezener, Eren, Huot, Fantine, Lamm, Matthew, De Cao, Nicola, Chen, Charlie, Mudgal, Sidharth, Stella, Romina, Brooks, Kevin, Vasudevan, Gautam, Liu, Chenxi, Chain, Mainak, Melinkeri, Nivedita, Cohen, Aaron, Wang, Venus, Seymore, Kristie, Zubkov, Sergey, Goel, Rahul, Yue, Summer, Krishnakumaran, Sai, Albert, Brian, Hurley, Nate, Sano, Motoki, Mohananey, Anhad, 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Shivanna, Rakesh, Chuklin, Aleksandr, Li, Josie, Spadine, Carrie, Wolfe, Travis, Mohamed, Kareem, Das, Subhabrata, Dai, Zihang, He, Kyle, von Dincklage, Daniel, Upadhyay, Shyam, Maurya, Akanksha, Chi, Luyan, Krause, Sebastian, Salama, Khalid, Rabinovitch, Pam G, M, Pavan Kumar Reddy, Selvan, Aarush, Dektiarev, Mikhail, Ghiasi, Golnaz, Guven, Erdem, Gupta, Himanshu, Liu, Boyi, Sharma, Deepak, Shtacher, Idan Heimlich, Paul, Shachi, Akerlund, Oscar, Aubet, François-Xavier, Huang, Terry, Zhu, Chen, Zhu, Eric, Teixeira, Elico, Fritze, Matthew, Bertolini, Francesco, Marinescu, Liana-Eleonora, Bölle, Martin, Paulus, Dominik, Gupta, Khyatti, Latkar, Tejasi, Chang, Max, Sanders, Jason, Wilson, Roopa, Wu, Xuewei, Tan, Yi-Xuan, Thiet, Lam Nguyen, Doshi, Tulsee, Lall, Sid, Mishra, Swaroop, Chen, Wanming, Luong, Thang, Benjamin, Seth, Lee, Jasmine, Andrejczuk, Ewa, Rabiej, Dominik, Ranjan, Vipul, Styrc, Krzysztof, Yin, Pengcheng, Simon, Jon, Harriott, Malcolm Rose, Bansal, Mudit, Robsky, Alexei, 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Matthew, Paszke, Adam, Chiu, Chung-Cheng, Elias, Jaume Sanchez, Mohiuddin, Afroz, Muhammad, Faizan, Miao, Jin, Lee, Andrew, Vieillard, Nino, Park, Jane, Zhang, Jiageng, Stanway, Jeff, Garmon, Drew, Karmarkar, Abhijit, Dong, Zhe, Lee, Jong, Kumar, Aviral, Zhou, Luowei, Evens, Jonathan, Isaac, William, Irving, Geoffrey, Loper, Edward, Fink, Michael, Arkatkar, Isha, Chen, Nanxin, Shafran, Izhak, Petrychenko, Ivan, Chen, Zhe, Jia, Johnson, Levskaya, Anselm, Zhu, Zhenkai, Grabowski, Peter, Mao, Yu, Magni, Alberto, Yao, Kaisheng, Snaider, Javier, Casagrande, Norman, Palmer, Evan, Suganthan, Paul, Castaño, Alfonso, Giannoumis, Irene, Kim, Wooyeol, Rybiński, Mikołaj, Sreevatsa, Ashwin, Prendki, Jennifer, Soergel, David, Goedeckemeyer, Adrian, Gierke, Willi, Jafari, Mohsen, Gaba, Meenu, Wiesner, Jeremy, Wright, Diana Gage, Wei, Yawen, Vashisht, Harsha, Kulizhskaya, Yana, Hoover, Jay, Le, Maigo, Li, Lu, Iwuanyanwu, Chimezie, Liu, Lu, Ramirez, Kevin, Khorlin, Andrey, Cui, Albert, LIN, Tian, Wu, Marcus, Aguilar, Ricardo, Pallo, Keith, Chakladar, Abhishek, Perng, Ginger, Abellan, Elena Allica, Zhang, Mingyang, Dasgupta, Ishita, Kushman, Nate, Penchev, Ivo, Repina, Alena, Wu, Xihui, van der Weide, Tom, Ponnapalli, Priya, Kaplan, Caroline, Simsa, Jiri, Li, Shuangfeng, Dousse, Olivier, Piper, Jeff, Ie, Nathan, Pasumarthi, Rama, Lintz, Nathan, Vijayakumar, Anitha, Andor, Daniel, Valenzuela, Pedro, Lui, Minnie, Paduraru, Cosmin, Peng, Daiyi, Lee, Katherine, Zhang, Shuyuan, Greene, Somer, Nguyen, Duc Dung, Kurylowicz, Paula, Hardin, Cassidy, Dixon, Lucas, Janzer, Lili, Choo, Kiam, Feng, Ziqiang, Zhang, Biao, Singhal, Achintya, Du, Dayou, McKinnon, Dan, Antropova, Natasha, Bolukbasi, Tolga, Keller, Orgad, Reid, David, Finchelstein, Daniel, Raad, Maria Abi, Crocker, Remi, Hawkins, Peter, Dadashi, Robert, Gaffney, Colin, Franko, Ken, Bulanova, Anna, Leblond, Rémi, Chung, Shirley, Askham, Harry, Cobo, Luis C., Xu, Kelvin, Fischer, Felix, Xu, Jun, Sorokin, Christina, Alberti, Chris, Lin, Chu-Cheng, Evans, Colin, Dimitriev, Alek, Forbes, Hannah, Banarse, Dylan, Tung, Zora, Omernick, Mark, Bishop, Colton, Sterneck, Rachel, Jain, Rohan, Xia, Jiawei, Amid, Ehsan, Piccinno, Francesco, Wang, Xingyu, Banzal, Praseem, Mankowitz, Daniel J., Polozov, Alex, Krakovna, Victoria, Brown, Sasha, Bateni, MohammadHossein, Duan, Dennis, Firoiu, Vlad, Thotakuri, Meghana, Natan, Tom, Geist, Matthieu, Girgin, Ser tan, Li, Hui, Ye, Jiayu, Roval, Ofir, Tojo, Reiko, Kwong, Michael, Lee-Thorp, James, Yew, Christopher, Sinopalnikov, Danila, Ramos, Sabela, Mellor, John, Sharma, Abhishek, Wu, Kathy, Miller, David, Sonnerat, Nicolas, Vnukov, Denis, Greig, Rory, Beattie, Jennifer, Caveness, Emily, Bai, Libin, Eisenschlos, Julian, Korchemniy, Alex, Tsai, Tomy, Jasarevic, Mimi, Kong, Weize, Dao, Phuong, Zheng, Zeyu, Liu, Frederick, Zhu, Rui, Teh, Tian Huey, Sanmiya, Jason, Gladchenko, Evgeny, Trdin, Nejc, Toyama, Daniel, Rosen, Evan, Tavakkol, Sasan, Xue, Linting, Elkind, Chen, Woodman, Oliver, Carpenter, John, Papamakarios, George, Kemp, Rupert, Kafle, Sushant, Grunina, Tanya, Sinha, Rishika, Talbert, Alice, Wu, Diane, Owusu-Afriyie, Denese, Thornton, Chloe, Pont-Tuset, Jordi, Narayana, Pradyumna, Li, Jing, Fatehi, Saaber, Wieting, John, Ajmeri, Omar, Uria, Benigno, Ko, Yeongil, Knight, Laura, Héliou, Amélie, Niu, Ning, Gu, Shane, Pang, Chenxi, Li, Yeqing, Levine, Nir, Stolovich, Ariel, Santamaria-Fernandez, Rebeca, Goenka, Sonam, Yustalim, Wenny, Strudel, Robin, Elqursh, Ali, Deck, Charlie, Lee, Hyo, Li, Zonglin, Levin, Kyle, Hoffmann, Raphael, Holtmann-Rice, Dan, Bachem, Olivier, Arora, Sho, Koh, Christy, Yeganeh, Soheil Hassas, Põder, Siim, Tariq, Mukarram, Sun, Yanhua, Ionita, Lucian, Seyedhosseini, Mojtaba, Tafti, Pouya, Liu, Zhiyu, Gulati, Anmol, Liu, Jasmine, Ye, Xinyu, Chrzaszcz, Bart, Wang, Lily, Sethi, Nikhil, Li, Tianrun, Brown, Ben, Singh, Shreya, Fan, Wei, Parisi, Aaron, Stanton, Joe, Koverkathu, Vinod, Choquette-Choo, Christopher A., Li, Yunjie, Lu, TJ, Shroff, Prakash, Varadarajan, Mani, Bahargam, Sanaz, Willoughby, Rob, Gaddy, David, Desjardins, Guillaume, Cornero, Marco, Robenek, Brona, Mittal, Bhavishya, Albrecht, Ben, Shenoy, Ashish, Moiseev, Fedor, Jacobsson, Henrik, Ghaffarkhah, Alireza, Rivière, Morgane, Walton, Alanna, Crepy, Clément, Parrish, Alicia, Zhou, Zongwei, Farabet, Clement, Radebaugh, Carey, Srinivasan, Praveen, van der Salm, Claudia, Fidjeland, Andreas, Scellato, Salvatore, Latorre-Chimoto, Eri, Klimczak-Plucińska, Hanna, Bridson, David, de Cesare, Dario, Hudson, Tom, Mendolicchio, Piermaria, Walker, Lexi, Morris, Alex, Mauger, Matthew, Guseynov, Alexey, Reid, Alison, Odoom, Seth, Loher, Lucia, Cotruta, Victor, Yenugula, Madhavi, Grewe, Dominik, Petrushkina, Anastasia, Duerig, Tom, Sanchez, Antonio, Yadlowsky, Steve, Shen, Amy, Globerson, Amir, Webb, Lynette, Dua, Sahil, Li, Dong, Bhupatiraju, Surya, Hurt, Dan, Qureshi, Haroon, Agarwal, Ananth, Shani, Tomer, Eyal, Matan, Khare, Anuj, Belle, Shreyas Rammohan, Wang, Lei, Tekur, Chetan, Kale, Mihir Sanjay, Wei, Jinliang, Sang, Ruoxin, Saeta, Brennan, Liechty, Tyler, Sun, Yi, Zhao, Yao, Lee, Stephan, Nayak, Pandu, Fritz, Doug, Vuyyuru, Manish Reddy, Aslanides, John, Vyas, Nidhi, Wicke, Martin, Ma, Xiao, Eltyshev, Evgenii, Martin, Nina, Cate, Hardie, Manyika, James, Amiri, Keyvan, Kim, Yelin, Xiong, Xi, Kang, Kai, Luisier, Florian, Tripuraneni, Nilesh, Madras, David, Guo, Mandy, Waters, Austin, Wang, Oliver, Ainslie, Joshua, Baldridge, Jason, Zhang, Han, Pruthi, Garima, Bauer, Jakob, Yang, Feng, Mansour, Riham, Gelman, Jason, Xu, Yang, Polovets, George, Liu, Ji, Cai, Honglong, Chen, Warren, Sheng, XiangHai, Xue, Emily, Ozair, Sherjil, Angermueller, Christof, Li, Xiaowei, Sinha, Anoop, Wang, Weiren, Wiesinger, Julia, Koukoumidis, Emmanouil, Tian, Yuan, Iyer, Anand, Gurumurthy, Madhu, Goldenson, Mark, Shah, Parashar, Blake, MK, Yu, Hongkun, Urbanowicz, Anthony, Palomaki, Jennimaria, Fernando, Chrisantha, Durden, Ken, Mehta, Harsh, Momchev, Nikola, Rahimtoroghi, Elahe, Georgaki, Maria, Raul, Amit, Ruder, Sebastian, Redshaw, Morgan, Lee, Jinhyuk, Zhou, Denny, Jalan, Komal, Li, Dinghua, Hechtman, Blake, Schuh, Parker, Nasr, Milad, Milan, Kieran, Mikulik, Vladimir, Franco, Juliana, Green, Tim, Nguyen, Nam, Kelley, Joe, Mahendru, Aroma, Hu, Andrea, Howland, Joshua, Vargas, Ben, Hui, Jeffrey, Bansal, Kshitij, Rao, Vikram, Ghiya, Rakesh, Wang, Emma, Ye, Ke, Sarr, Jean Michel, Preston, Melanie Moranski, Elish, Madeleine, Li, Steve, Kaku, Aakash, Gupta, Jigar, Pasupat, Ice, Juan, Da-Cheng, Someswar, Milan, M., Tejvi, Chen, Xinyun, Amini, Aida, Fabrikant, Alex, Chu, Eric, Dong, Xuanyi, Muthal, Amruta, Buthpitiya, Senaka, Jauhari, Sarthak, Khandelwal, Urvashi, Hitron, Ayal, Ren, Jie, Rinaldi, Larissa, Drath, Shahar, Dabush, Avigail, Jiang, Nan-Jiang, Godhia, Harshal, Sachs, Uli, Chen, Anthony, Fan, Yicheng, Taitelbaum, Hagai, Noga, Hila, Dai, Zhuyun, Wang, James, Hamer, Jenny, Ferng, Chun-Sung, Elkind, Chenel, Atias, Aviel, Lee, Paulina, Listík, Vít, Carlen, Mathias, van de Kerkhof, Jan, Pikus, Marcin, Zaher, Krunoslav, Müller, Paul, Zykova, Sasha, Stefanec, Richard, Gatsko, Vitaly, Hirnschall, Christoph, Sethi, Ashwin, Xu, Xingyu Federico, Ahuja, Chetan, Tsai, Beth, Stefanoiu, Anca, Feng, Bo, Dhandhania, Keshav, Katyal, Manish, Gupta, Akshay, Parulekar, Atharva, Pitta, Divya, Zhao, Jing, Bhatia, Vivaan, Bhavnani, Yashodha, Alhadlaq, Omar, Li, Xiaolin, Danenberg, Peter, Tu, Dennis, Pine, Alex, Filippova, Vera, Ghosh, Abhipso, Limonchik, Ben, Urala, Bhargava, Lanka, Chaitanya Krishna, Clive, Derik, Li, Edward, Wu, Hao, Hongtongsak, Kevin, Li, Ianna, Thakkar, Kalind, Omarov, Kuanysh, Majmundar, Kushal, Alverson, Michael, Kucharski, Michael, Patel, Mohak, Jain, Mudit, Zabelin, Maksim, Pelagatti, Paolo, Kohli, Rohan, Kumar, Saurabh, Kim, Joseph, Sankar, Swetha, Shah, Vineet, Ramachandruni, Lakshmi, Zeng, Xiangkai, Bariach, Ben, Weidinger, Laura, Vu, Tu, Andreev, Alek, He, Antoine, Hui, Kevin, Kashem, Sheleem, Subramanya, Amar, Hsiao, Sissie, Hassabis, Demis, Kavukcuoglu, Koray, Sadovsky, Adam, Le, Quoc, Strohman, Trevor, Wu, Yonghui, Petrov, Slav, Dean, Jeffrey, and Vinyals, Oriol
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.
Published: 2023

9. Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Author: Collaboration, Open X-Embodiment, O'Neill, Abby, Rehman, Abdul, Gupta, Abhinav, Maddukuri, Abhiram, Gupta, Abhishek, Padalkar, Abhishek, Lee, Abraham, Pooley, Acorn, Gupta, Agrim, Mandlekar, Ajay, Jain, Ajinkya, Tung, Albert, Bewley, Alex, Herzog, Alex, Irpan, Alex, Khazatsky, Alexander, Rai, Anant, Gupta, Anchit, Wang, Andrew, Kolobov, Andrey, Singh, Anikait, Garg, Animesh, Kembhavi, Aniruddha, Xie, Annie, Brohan, Anthony, Raffin, Antonin, Sharma, Archit, Yavary, Arefeh, Jain, Arhan, Balakrishna, Ashwin, Wahid, Ayzaan, Burgess-Limerick, Ben, Kim, Beomjoon, Schölkopf, Bernhard, Wulfe, Blake, Ichter, Brian, Lu, Cewu, Xu, Charles, Le, Charlotte, Finn, Chelsea, Wang, Chen, Xu, Chenfeng, Chi, Cheng, Huang, Chenguang, Chan, Christine, Agia, Christopher, Pan, Chuer, Fu, Chuyuan, Devin, Coline, Xu, Danfei, Morton, Daniel, Driess, Danny, Chen, Daphne, Pathak, Deepak, Shah, Dhruv, Büchler, Dieter, Jayaraman, Dinesh, Kalashnikov, Dmitry, Sadigh, Dorsa, Johns, Edward, Foster, Ethan, Liu, Fangchen, Ceola, Federico, Xia, Fei, Zhao, Feiyu, Frujeri, Felipe Vieira, Stulp, Freek, Zhou, Gaoyue, Sukhatme, Gaurav S., Salhotra, Gautam, Yan, Ge, Feng, Gilbert, Schiavi, Giulio, Berseth, Glen, Kahn, Gregory, Yang, Guangwen, Wang, Guanzhi, Su, Hao, Fang, Hao-Shu, Shi, Haochen, Bao, Henghui, Amor, Heni Ben, Christensen, Henrik I, Furuta, Hiroki, Bharadhwaj, Homanga, Walke, Homer, Fang, Hongjie, Ha, Huy, Mordatch, Igor, Radosavovic, Ilija, Leal, Isabel, Liang, Jacky, Abou-Chakra, Jad, Kim, Jaehyung, Drake, Jaimyn, Peters, Jan, Schneider, Jan, Hsu, Jasmine, Vakil, Jay, Bohg, Jeannette, Bingham, Jeffrey, Wu, Jeffrey, Gao, Jensen, Hu, Jiaheng, Wu, Jiajun, Wu, Jialin, Sun, Jiankai, Luo, Jianlan, Gu, Jiayuan, Tan, Jie, Oh, Jihoon, Wu, Jimmy, Lu, Jingpei, Yang, Jingyun, Malik, Jitendra, Silvério, João, Hejna, Joey, Booher, Jonathan, Tompson, Jonathan, Yang, Jonathan, Salvador, Jordi, Lim, Joseph J., Han, Junhyek, Wang, Kaiyuan, Rao, Kanishka, Pertsch, Karl, Hausman, Karol, Go, Keegan, Gopalakrishnan, Keerthana, Goldberg, Ken, Byrne, Kendra, Oslund, Kenneth, Kawaharazuka, Kento, Black, Kevin, Lin, Kevin, Zhang, Kevin, Ehsani, Kiana, Lekkala, Kiran, Ellis, Kirsty, Rana, Krishan, Srinivasan, Krishnan, Fang, Kuan, Singh, Kunal Pratap, Zeng, Kuo-Hao, Hatch, Kyle, Hsu, Kyle, Itti, Laurent, Chen, Lawrence Yunliang, Pinto, Lerrel, Fei-Fei, Li, Tan, Liam, Fan, Linxi "Jim", Ott, Lionel, Lee, Lisa, Weihs, Luca, Chen, Magnum, Lepert, Marion, Memmel, Marius, Tomizuka, Masayoshi, Itkina, Masha, Castro, Mateo Guaman, Spero, Max, Du, Maximilian, Ahn, Michael, Yip, Michael C., Zhang, Mingtong, Ding, Mingyu, Heo, Minho, Srirama, Mohan Kumar, Sharma, Mohit, Kim, Moo Jin, Kanazawa, Naoaki, Hansen, Nicklas, Heess, Nicolas, Joshi, Nikhil J, Suenderhauf, Niko, Liu, Ning, Di Palo, Norman, Shafiullah, Nur Muhammad Mahi, Mees, Oier, Kroemer, Oliver, Bastani, Osbert, Sanketi, Pannag R, Miller, Patrick "Tree", Yin, Patrick, Wohlhart, Paul, Xu, Peng, Fagan, Peter David, Mitrano, Peter, Sermanet, Pierre, Abbeel, Pieter, Sundaresan, Priya, Chen, Qiuyu, Vuong, Quan, Rafailov, Rafael, Tian, Ran, Doshi, Ria, Mart'in-Mart'in, Roberto, Baijal, Rohan, Scalise, Rosario, Hendrix, Rose, Lin, Roy, Qian, Runjia, Zhang, Ruohan, Mendonca, Russell, Shah, Rutav, Hoque, Ryan, Julian, Ryan, Bustamante, Samuel, Kirmani, Sean, Levine, Sergey, Lin, Shan, Moore, Sherry, Bahl, Shikhar, Dass, Shivin, Sonawani, Shubham, Tulsiani, Shubham, Song, Shuran, Xu, Sichun, Haldar, Siddhant, Karamcheti, Siddharth, Adebola, Simeon, Guist, Simon, Nasiriany, Soroush, Schaal, Stefan, Welker, Stefan, Tian, Stephen, Ramamoorthy, Subramanian, Dasari, Sudeep, Belkhale, Suneel, Park, Sungjae, Nair, Suraj, Mirchandani, Suvir, Osa, Takayuki, Gupta, Tanmay, Harada, Tatsuya, Matsushima, Tatsuya, Xiao, Ted, Kollar, Thomas, Yu, Tianhe, Ding, Tianli, Davchev, Todor, Zhao, Tony Z., Armstrong, Travis, Darrell, Trevor, Chung, Trinity, Jain, Vidhi, Kumar, Vikash, Vanhoucke, Vincent, Zhan, Wei, Zhou, Wenxuan, Burgard, Wolfram, Chen, Xi, Chen, Xiangyu, Wang, Xiaolong, Zhu, Xinghao, Geng, Xinyang, Liu, Xiyuan, Liangwei, Xu, Li, Xuanlin, Pang, Yansong, Lu, Yao, Ma, Yecheng Jason, Kim, Yejin, Chebotar, Yevgen, Zhou, Yifan, Zhu, Yifeng, Wu, Yilin, Xu, Ying, Wang, Yixuan, Bisk, Yonatan, Dou, Yongqiang, Cho, Yoonyoung, Lee, Youngwoon, Cui, Yuchen, Cao, Yue, Wu, Yueh-Hua, Tang, Yujin, Zhu, Yuke, Zhang, Yunchu, Jiang, Yunfan, Li, Yunshuang, Li, Yunzhu, Iwasawa, Yusuke, Matsuo, Yutaka, Ma, Zehan, Xu, Zhuo, Cui, Zichen Jeff, Zhang, Zichen, Fu, Zipeng, and Lin, Zipeng
Subjects: Computer Science - Robotics
Abstract: Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website https://robotics-transformer-x.github.io., Comment: Project website: https://robotics-transformer-x.github.io
Published: 2023

10. Paradise Cove

Author: Lee, Lisa
Published: 2014
Full Text: View/download PDF

11. Guide Your Agent with Adaptive Multimodal Rewards

Author: Kim, Changyeon, Seo, Younggyo, Liu, Hao, Lee, Lisa, Shin, Jinwoo, Lee, Honglak, and Lee, Kimin
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract: Developing an agent capable of adapting to unseen environments remains a difficult challenge in imitation learning. This work presents Adaptive Return-conditioned Policy (ARP), an efficient framework designed to enhance the agent's generalization ability using natural language task descriptions and pre-trained multimodal encoders. Our key idea is to calculate a similarity between visual observations and natural language instructions in the pre-trained multimodal embedding space (such as CLIP) and use it as a reward signal. We then train a return-conditioned policy using expert demonstrations labeled with multimodal rewards. Because the multimodal rewards provide adaptive signals at each timestep, our ARP effectively mitigates the goal misgeneralization. This results in superior generalization performances even when faced with unseen text instructions, compared to existing text-conditioned policies. To improve the quality of rewards, we also introduce a fine-tuning method for pre-trained multimodal encoders, further enhancing the performance. Video demonstrations and source code are available on the project website: \url{https://sites.google.com/view/2023arp}., Comment: Accepted to NeurIPS 2023. Project webpage: https://sites.google.com/view/2023arp
Published: 2023

12. RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Author: Brohan, Anthony, Brown, Noah, Carbajal, Justice, Chebotar, Yevgen, Chen, Xi, Choromanski, Krzysztof, Ding, Tianli, Driess, Danny, Dubey, Avinava, Finn, Chelsea, Florence, Pete, Fu, Chuyuan, Arenas, Montse Gonzalez, Gopalakrishnan, Keerthana, Han, Kehang, Hausman, Karol, Herzog, Alexander, Hsu, Jasmine, Ichter, Brian, Irpan, Alex, Joshi, Nikhil, Julian, Ryan, Kalashnikov, Dmitry, Kuang, Yuheng, Leal, Isabel, Lee, Lisa, Lee, Tsang-Wei Edward, Levine, Sergey, Lu, Yao, Michalewski, Henryk, Mordatch, Igor, Pertsch, Karl, Rao, Kanishka, Reymann, Krista, Ryoo, Michael, Salazar, Grecia, Sanketi, Pannag, Sermanet, Pierre, Singh, Jaspiar, Singh, Anikait, Soricut, Radu, Tran, Huong, Vanhoucke, Vincent, Vuong, Quan, Wahid, Ayzaan, Welker, Stefan, Wohlhart, Paul, Wu, Jialin, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Sichun, Yu, Tianhe, and Zitkovich, Brianna
Subjects: Computer Science - Robotics, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows that our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot training data (such as placing an object onto a particular number or icon), and the ability to perform rudimentary reasoning in response to user commands (such as picking up the smallest or largest object, or the one closest to another object). We further show that incorporating chain of thought reasoning allows RT-2 to perform multi-stage semantic reasoning, for example figuring out which object to pick up for use as an improvised hammer (a rock), or which type of drink is best suited for someone who is tired (an energy drink)., Comment: Website: https://robotics-transformer.github.io/
Published: 2023

13. Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation

Author: Xie, Annie, Lee, Lisa, Xiao, Ted, and Finn, Chelsea
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract: What makes generalization hard for imitation learning in visual robotic manipulation? This question is difficult to approach at face value, but the environment from the perspective of a robot can often be decomposed into enumerable factors of variation, such as the lighting conditions or the placement of the camera. Empirically, generalization to some of these factors have presented a greater obstacle than others, but existing work sheds little light on precisely how much each factor contributes to the generalization gap. Towards an answer to this question, we study imitation learning policies in simulation and on a real robot language-conditioned manipulation task to quantify the difficulty of generalization to different (sets of) factors. We also design a new simulated benchmark of 19 tasks with 11 factors of variation to facilitate more controlled evaluations of generalization. From our study, we determine an ordering of factors based on generalization difficulty, that is consistent across simulation and our real robot setup., Comment: Project webpage at https://sites.google.com/view/generalization-gap
Published: 2023

14. SARS-CoV-2 Surveillance of Wild Mice and Rats in North American Cities

Author: Lee, Lisa K. F., Himsworth, Chelsea G., Prystajecky, Natalie, Dibernardo, Antonia, Lindsay, L. Robbin, Albers, Theresa M., Dhawan, Rajeev, Henderson, Ken, Mulder, Guy, Atwal, Harveen K., Beattie, Imara, Wobeser, Bruce K., Parsons, Michael H., and Byers, Kaylee A.
Published: 2024
Full Text: View/download PDF

15. Barkour: Benchmarking Animal-level Agility with Quadruped Robots

Author: Caluwaerts, Ken, Iscen, Atil, Kew, J. Chase, Yu, Wenhao, Zhang, Tingnan, Freeman, Daniel, Lee, Kuang-Huei, Lee, Lisa, Saliceti, Stefano, Zhuang, Vincent, Batchelor, Nathan, Bohez, Steven, Casarini, Federico, Chen, Jose Enrique, Cortes, Omar, Coumans, Erwin, Dostmohamed, Adil, Dulac-Arnold, Gabriel, Escontrela, Alejandro, Frey, Erik, Hafner, Roland, Jain, Deepali, Jyenis, Bauyrjan, Kuang, Yuheng, Lee, Edward, Luu, Linda, Nachum, Ofir, Oslund, Ken, Powell, Jason, Reyes, Diego, Romano, Francesco, Sadeghi, Feresteh, Sloat, Ron, Tabanpour, Baruch, Zheng, Daniel, Neunert, Michael, Hadsell, Raia, Heess, Nicolas, Nori, Francesco, Seto, Jeff, Parada, Carolina, Sindhwani, Vikas, Vanhoucke, Vincent, and Tan, Jie
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract: Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biological counterparts and show various agile skills to navigate complex environments quickly. Despite the interest, the field lacks systematic benchmarks to measure the performance of control policies and hardware in agility. We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots. Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism. This encourages researchers to develop controllers that not only move fast, but do so in a controllable and versatile way. To set strong baselines, we present two methods for tackling the benchmark. In the first approach, we train specialist locomotion skills using on-policy reinforcement learning methods and combine them with a high-level navigation controller. In the second approach, we distill the specialist skills into a Transformer-based generalist locomotion policy, named Locomotion-Transformer, that can handle various terrains and adjust the robot's gait based on the perceived environment and robot states. Using a custom-built quadruped robot, we demonstrate that our method can complete the course at half the speed of a dog. We hope that our work represents a step towards creating controllers that enable robots to reach animal-level agility., Comment: 17 pages, 19 figures
Published: 2023

16. Awake Supraglottic Airway Placement in Pediatric Patients for Airway Obstruction or Difficult Intubation: Insights From an International Airway Registry (PeDI)

Author: Longacre, Mckenna, Park, Raymond S., Staffa, Steven J., Rowland, Matthew J., Meserve, Jonathan, Lord, Charles, Templeton, T. Wesley, Garcia-Marcinkiewicz, Annery G., Peyton, James M., Fiadjoe, John E., Kovatsis, Pete G., Stein, Mary Lyn, Bruins, Benjamin B., Stricker, Paul, Laverriere, Elizabeth K., Lockman, Justin L., Struyk, Brian, Ward, Christopher, Nishisaki, Akira, Kodavatiganti, Ramesh, Guris, Rodrigo J. Daly, Sequera-Ramos, Luis, Teen, Mark S., Oke, Ayodele, Hsu, Grace, Lingappan, Arul, Egbuta, Chinyere, Flynn, Stephen, Sarmiento, Lina, Battles, Rhae, Bocanegra, Ashley D., Goldfarb, Tally, Kiss, Edgar E., Olomu, Patrick N., Szmuk, Peter, Mireles, Sam, Murray, Andrea, Whyte, Simon, Jain, Ranu, Khan, Sabina A., Matuszczak, Maria, Hunyady, Agnes, Holmes, Christopher, McCann, Alexander, Sabato, Stefano, Matava, Clyde, Dalesio, Nicholas, Greenberg, Robert, Lucero, Angela, Desai, Sapna, Tennessee, Nashville, Rosander, Sondra, Samba, Sindhu, Schrock, Charles, Nykiel-Bailey, Sydney, Marsh, Jennifer, Brooks Peterson, Melissa, Johnson Lee, Amy Colleen, Bhattacharya, Somaletha, Burjek, Nicholas E., Jagannathan, Narasimhan, Lardner, David, Crockett, Christy, Robertson, Sara, Sathyamoorthy, Madhankumar, Chiao, Franklin, Patel, Jasmine, Sharma, Aarti, Echeverry Marin, Piedad, Pérez-Pradilla, Carolina, Singh, Neeta, Taicher, Brad, von Ungern-Sternberg, Britta S., Sommerfield, David, Hauser, Neil, Hesselink, Emily, Lewkowitz-Shpuntoff, Hilana, Castro, Pilar, Riveros Perez, N. Ricardo, Leite, Fernanda, Vega, Eduardo, González, Alejandro, Ostermann, Paola, Rubin, Kasia, Lee, Angela, Valairucha, Songyos, Dalal, Priti, Tran, Thanh, Anspach, Taylor, Lee, Lisa K., Ayad, Ihab, Rehman, Mohamed, Fernandez, Allison, Zamora, Lillian, Ravula, Niroop, Shaik, Sadiq, Szolnoki, Judit, Mathew, Preethy J., Yaddanapudi, Sandhya, Sen, Indu, Gupta, Aakriti, Handlogten, Kathryn, Sroka, J. Michael, Caldeira Quintao, Vinicius, and Vieira Carlos, Ricardo
Published: 2024
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17. Difficult or impossible facemask ventilation in children with difficult tracheal intubation: a retrospective analysis of the PeDI registry

Author: Garcia-Marcinkiewicz, Annery G, Lee, Lisa K, Haydar, Bishr, Fiadjoe, John E, Matava, Clyde T, Kovatsis, Pete G, Peyton, James, Stein, Mary L, Park, Raymond, Taicher, Brad M, Templeton, Thomas W, Collaborative, on behalf of the PeDI, Bruins, Benjamin B, Stricker, Paul, Laverriere, Elizabeth K, Lockman, Justin L, Struyk, Brian, Ward, Christopher, Nishisaki, Akira, Kodavatiganti, Ramesh, Guris, Rodrigo J Daly, Sequera-Ramos, Luis, Teen, Mark S, Oke, Ayodele, Hsu, Grace, Lingappan, Arul, Egbuta, Chinyere, Flynn, Stephen, Sarmiento, Lina, Goldfarb, Tally, Kiss, Edgar E, Olomu, Patrick N, Szmuk, Peter, Mireles, Sam, Murray, Andrea, Whyte, Simon, Jain, Ranu, Matuszczak, Maria, Hunyady, Agnes, Bosenberg, Adrian, Tham, See, Low, Daniel, Holmes, Christopher, Sabato, Stefan, Dalesio, Nicholas, Greenberg, Robert, Lucero, Angela, Reynolds, Paul, Lewis, Ian, Schrock, Charles, Nykiel-Bailey, Sydney, Starker, Elizabeth, Szolnoki, Judit, Brooks-Peterson, Melissa, Bhattacharya, Somaletha, Burjek, Nicholas E, Jagannathan, Narasimhan, Lardner, David, Watkins, Scott, Crockett, Christy, Moore, John, Robertson, Sara, Sathyamoorthy, Madhankumar, Chiao, Franklin, Patel, Jasmine, Sharma, Aarti, Marin, Piedad Echeverry, Pérez-Pradilla, Carolina, Singh, Neeta, von Ungern-Sternberg, Britta S, Sommerfield, David, Bilen-Rosas, Guelay, Lewkowitz-Shpuntoff, Hilana, Castro, Pilar, Perez, N Ricardo Riveros, de Graaff, Jurgen C, Vega, Eduardo, González, Alejandro, Ostermann, Paola, Rubin, Kasia, Lord, Charles, Lee, Angela, Heitmiller, Eugenie, Valairucha, Songyos, Dalal, Priti, Tran, Thanh, Ayad, Ihab, Rehman, Mohamed, Fernandez, Allison, Zamora, Lillian, Ravula, Niroop, and Shaik, Sadiq
Subjects: Rare Diseases, Lung, Assistive Technology, Bioengineering, Infant, Humans, Child, Masks, Intubation, Intratracheal, Retrospective Studies, Respiration, Laryngeal Masks, Airway Management, complications, difficult airway, difficult facemask ventilation, impossible facemask ventilation, paediatrics, supraglottic airway, PeDI Collaborative, Clinical Sciences, Anesthesiology
Abstract: BackgroundDifficult facemask ventilation is perilous in children whose tracheas are difficult to intubate. We hypothesised that certain physical characteristics and anaesthetic factors are associated with difficult mask ventilation in paediatric patients who also had difficult tracheal intubation.MethodsWe queried a multicentre registry for children who experienced "difficult" or "impossible" facemask ventilation. Patient and case factors known before mask ventilation attempt were included for consideration in this regularised multivariable regression analysis. Incidence of complications, and frequency and efficacy of rescue placement of a supraglottic airway device were also tabulated. Changes in quality of mask ventilation after injection of a neuromuscular blocking agent were assessed.ResultsThe incidence of difficult mask ventilation was 9% (483 of 5453 patients). Infants and patients having increased weight, being less than 5th percentile in weight for age, or having Treacher-Collins syndrome, glossoptosis, or limited mouth opening were more likely to have difficult mask ventilation. Anaesthetic induction using facemask and opioids was associated with decreased risk of difficult mask ventilation. The incidence of complications was significantly higher in patients with "difficult" mask ventilation than in patients without. Rescue placement of a supraglottic airway improved ventilation in 71% (96 of 135) of cases. Administration of neuromuscular blocking agents was more frequently associated with improvement or no change in quality of ventilation than with worsening.ConclusionsCertain abnormalities on physical examination should increase suspicion of possible difficult facemask ventilation. Rescue use of a supraglottic airway device in children with difficult or impossible mask ventilation should be strongly considered.
Published: 2023

18. Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models

Author: Liu, Hao, Geng, Xinyang, Lee, Lisa, Mordatch, Igor, Levine, Sergey, Narang, Sharan, and Abbeel, Pieter
Subjects: Computer Science - Computation and Language
Abstract: Large language models (LLM) trained using the next-token-prediction objective, such as GPT3 and PaLM, have revolutionized natural language processing in recent years by showing impressive zero-shot and few-shot capabilities across a wide range of tasks. In this work, we propose a simple technique that significantly boosts the performance of LLMs without adding computational cost. Our key observation is that, by performing the next token prediction task with randomly selected past tokens masked out, we can improve the quality of the learned representations for downstream language understanding tasks. We hypothesize that randomly masking past tokens prevents over-attending to recent tokens and encourages attention to tokens in the distant past. We find that our method, Forgetful Causal Masking (FCM), significantly improves both few-shot and finetuning performance of PaLM. We further consider a simple extension, T-FCM, which introduces bidirectional context to causal language model without altering the sequence order, and further improves finetuning performance., Comment: Added T-FCM and better FCM results
Published: 2022

19. Instruction-Following Agents with Multimodal Transformer

Author: Liu, Hao, Lee, Lisa, Lee, Kimin, and Abbeel, Pieter
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract: Humans are excellent at understanding language and vision to accomplish a wide range of tasks. In contrast, creating general instruction-following embodied agents remains a difficult challenge. Prior work that uses pure language-only models lack visual grounding, making it difficult to connect language instructions with visual observations. On the other hand, methods that use pre-trained multimodal models typically come with divided language and visual representations, requiring designing specialized network architecture to fuse them together. We propose a simple yet effective model for robots to solve instruction-following tasks in vision-based environments. Our \ours method consists of a multimodal transformer that encodes visual observations and language instructions, and a transformer-based policy that predicts actions based on encoded representations. The multimodal transformer is pre-trained on millions of image-text pairs and natural language text, thereby producing generic cross-modal representations of observations and instructions. The transformer-based policy keeps track of the full history of observations and actions, and predicts actions autoregressively. Despite its simplicity, we show that this unified transformer model outperforms all state-of-the-art pre-trained or trained-from-scratch methods in both single-task and multi-task settings. Our model also shows better model scalability and generalization ability than prior work., Comment: fixed a typo in affiliation
Published: 2022

20. Spectral Decomposition Representation for Reinforcement Learning

Author: Ren, Tongzheng, Zhang, Tianjun, Lee, Lisa, Gonzalez, Joseph E., Schuurmans, Dale, and Dai, Bo
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning
Abstract: Representation learning often plays a critical role in reinforcement learning by managing the curse of dimensionality. A representative class of algorithms exploits a spectral decomposition of the stochastic transition dynamics to construct representations that enjoy strong theoretical properties in an idealized setting. However, current spectral methods suffer from limited applicability because they are constructed for state-only aggregation and derived from a policy-dependent transition kernel, without considering the issue of exploration. To address these issues, we propose an alternative spectral method, Spectral Decomposition Representation (SPEDER), that extracts a state-action abstraction from the dynamics without inducing spurious dependence on the data collection policy, while also balancing the exploration-versus-exploitation trade-off during learning. A theoretical analysis establishes the sample efficiency of the proposed algorithm in both the online and offline settings. In addition, an experimental investigation demonstrates superior performance over current state-of-the-art algorithms across several benchmarks., Comment: ICLR 2023. The first two authors contribute equally
Published: 2022

21. Multi-Game Decision Transformers

Author: Lee, Kuang-Huei, Nachum, Ofir, Yang, Mengjiao, Lee, Lisa, Freeman, Daniel, Xu, Winnie, Guadarrama, Sergio, Fischer, Ian, Jang, Eric, Michalewski, Henryk, and Mordatch, Igor
Subjects: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: A longstanding goal of the field of AI is a method for learning a highly capable, generalist agent from diverse experience. In the subfields of vision and language, this was largely achieved by scaling up transformer-based models and training them on large, diverse datasets. Motivated by this progress, we investigate whether the same strategy can be used to produce generalist reinforcement learning agents. Specifically, we show that a single transformer-based model - with a single set of weights - trained purely offline can play a suite of up to 46 Atari games simultaneously at close-to-human performance. When trained and evaluated appropriately, we find that the same trends observed in language and vision hold, including scaling of performance with model size and rapid adaptation to new games via fine-tuning. We compare several approaches in this multi-game setting, such as online and offline RL methods and behavioral cloning, and find that our Multi-Game Decision Transformer models offer the best scalability and performance. We release the pre-trained models and code to encourage further research in this direction., Comment: NeurIPS 2022. 24 pages, 16 figures. Additional information, videos and code can be seen at https://sites.google.com/view/multi-game-transformers
Published: 2022

22. Multimodal Masked Autoencoders Learn Transferable Representations

Author: Geng, Xinyang, Liu, Hao, Lee, Lisa, Schuurmans, Dale, Levine, Sergey, and Abbeel, Pieter
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Building scalable models to learn from diverse, multimodal data remains an open challenge. For vision-language data, the dominant approaches are based on contrastive learning objectives that train a separate encoder for each modality. While effective, contrastive learning approaches introduce sampling bias depending on the data augmentations used, which can degrade performance on downstream tasks. Moreover, these methods are limited to paired image-text data, and cannot leverage widely-available unpaired data. In this paper, we investigate whether a large multimodal model trained purely via masked token prediction, without using modality-specific encoders or contrastive learning, can learn transferable representations for downstream tasks. We propose a simple and scalable network architecture, the Multimodal Masked Autoencoder (M3AE), which learns a unified encoder for both vision and language data via masked token prediction. We provide an empirical study of M3AE trained on a large-scale image-text dataset, and find that M3AE is able to learn generalizable representations that transfer well to downstream tasks. Surprisingly, we find that M3AE benefits from a higher text mask ratio (50-90%), in contrast to BERT whose standard masking ratio is 15%, due to the joint training of two data modalities. We also provide qualitative analysis showing that the learned representation incorporates meaningful information from both image and language. Lastly, we demonstrate the scalability of M3AE with larger model size and training time, and its flexibility to train on both paired image-text data as well as unpaired data.
Published: 2022

23. Capillaria hepatica (syn. Calodium hepaticum) infection and factors influencing infection carriage in rats (Rattus spp.) in Hong Kong

Author: Sandy, Jeanine, Uea-Anuwong, Theethawat, Kiu, Lam Hoi, Lee, Lisa K.F., Abdullah, Swaid, and Magouras, Ioannis
Published: 2024
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24. Risk factors for administration of additional reversal following neuromuscular blockade with rocuronium in children: A retrospective case–control study

Author: Vishneski, Susan R, Saha, Amit K, Fram, Madeline R, Templeton, Leah B, Lee, Lisa K, Ririe, Douglas G, Goenaga‐Diaz, Eduardo Javier, Smith, Laura Daniela, and Templeton, Thomas Wesley
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Pediatric, Clinical Research, Clinical Trials and Supportive Activities, Prevention, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Androstanols, Anesthetics, Case-Control Studies, Child, Humans, Neostigmine, Neuromuscular Blockade, Neuromuscular Diseases, Neuromuscular Nondepolarizing Agents, Retrospective Studies, Risk Factors, Rocuronium, gamma-Cyclodextrins, delayed emergence from anesthesia, African American, rocuronium, neostigmine, neuromuscular blockade, Paediatrics and Reproductive Medicine, Anesthesiology, Clinical sciences, Paediatrics
Abstract: BackgroundThe prevalence and risk factors for residual neuromuscular blockade in children remain poorly characterized. We hypothesize that specific patient and anesthetic risk factors may be associated with the administration of additional reversal in children following initial reversal of rocuronium with neostigmine.MethodsOur electronic health record was queried for patients 1 rocuronium dose administered during the procedure, initial neostigmine dose 0.45 mg kg-1 hr-1 . These were associated with the administration of additional reversal with an OR 1.52 (95% CI, 1.08-2.35) and OR 1.71 (95% CI, 1.10-2.67), respectively. Other risk factors included an initial neostigmine dose 0.45 mg kg-1 hr-1 , initial neostigmine dose
Published: 2022

25. Effectiveness of a Peer-Led Gatekeeper Program: A Longitudinal Mixed-Method Analysis

Author: Muehlenkamp, Jennifer J. and Quinn-Lee, Lisa
Abstract: Objective: The current study evaluated the long-term effectiveness of a peer delivered suicide prevention gatekeeper training program for college students. Participants: 1,345 undergraduate students (M[subscript age] = 20.24, SD = 3.27; 89.6% White) received the peer led training during a single class-period. Method: Participants completed pre- post- and 3-month follow-up surveys assessing knowledge, perceived intervention skills, willingness to intervene, and self-efficacy. Engagement in gatekeeper behaviors were assessed at follow-up. Two open-ended questions provided data for qualitative analysis. Results: Both quantitative and qualitative data indicated that participants showed substantial increases in all outcomes from pre- to post-training, and these gains were maintained at follow-up. Participants reported engaging in gatekeeper intervention behaviors at follow-up and qualitative results provide evidence this was due to the training. Conclusion: Peer led suicide prevention gatekeeper training is feasible, appears to be effective, and can enhance campus' capacity to provide sustainable suicide prevention programming on campus.
Published: 2023
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26. Art-Based Mindfulness at School: A Culturally Responsive Approach to School Mental Health

Author: Jones, Janine M. and Lee, Lisa H.
Abstract: As schools are increasingly tasked with improving student outcomes related to both academic and social and emotional wellbeing, digital interventions are becoming utilized as viable tools for successful outcomes. This study aimed to measure the effectiveness of a culturally responsive, art-based mindfulness intervention called L.A.U.G.H.® (Let Art Unleash Great Happiness), in a diverse sample of 243 elementary school students. Using an iPad app, students practiced mindful breathing and created digital art in their classrooms. The app also measured students' moods, school connectedness, and joy of learning. The study design was a one-group repeated measure intervention design where ratings were compared between Weeks 1, 4, 8, and 12 of the intervention. Results indicated that L.A.U.G.H.® time improved and increased students' moods, School Connectedness, and Joy of Learning over time. Race played a significant role in how students rated their feelings about school, with significant effects that emerged among Black/African American and Asian American/Pacific Islander students. Both groups began with the lowest ratings of school connectedness and demonstrated significant improvements by Week 8. This study revealed results from a digital mindfulness intervention and highlighted the importance of a nuanced study of differential impacts of such interventions among diverse students.
Published: 2022
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27. Temperature fluctuations during embryo transfer can be mitigated by optimizing transfer protocol

Author: Gurner, Kathryn H., Kelley, Rebecca L., Lee, Lisa Y.S., and Gardner, David K.
Published: 2024
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28. 2D magnesium phosphate resorbable coating to enhance cell adhesion on titanium surfaces

Author: Elhadad, Amir, Mezour, Mohamed A., Abu Nada, Lina, Shurbaji, Samar, Mansour, Alaa, Smith, Sophia, Moussa, Hanan, lee, Lisa, Pérez-Soriano, Eva M., Murshed, Monzur, Chromik, Richard, and Tamimi, Faleh
Published: 2024
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29. Reference services for students studying by distance : a comparative study of the attitudes distance students have towards phone, email and chat reference services

Author: Lee, Lisa Sandra
Published: 2008

30. A novel miRNA biomarker panel associated with mortality in pediatric patients with ARDS

Author: Lee, Lisa K, Eghbali, Mansoureh, and Sapru, Anil
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Pediatric, Acute Respiratory Distress Syndrome, Rare Diseases, Clinical Research, Lung, Genetics, Biotechnology, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Respiratory, Good Health and Well Being, Biomarkers, Child, Child, Preschool, Female, Humans, Male, MicroRNAs, Respiratory Distress Syndrome, Survival Rate, miRNA, ARDS, Pediatrics, Cardiorespiratory Medicine and Haematology, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract: We identified a novel microRNA biomarker panel consisting of 6 microRNAs predicting mortality in pediatric acute respiratory distress syndrome patients. Each of the identified mRNA have potential mechanistic importance in acute respiratory distress syndrome and may lead to the development of pharmacologic targets.
Published: 2021

31. Hypoxemia in Young Children Undergoing One-lung Ventilation: A Retrospective Cohort Study

Author: Templeton, T Wesley, Miller, Scott A, Lee, Lisa K, Kheterpal, Sachin, Mathis, Michael R, Goenaga-Díaz, Eduardo J, Templeton, Leah B, and Saha, Amit K
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Assistive Technology, Lung, Patient Safety, Bioengineering, Clinical Research, Respiratory, Child, Preschool, Cohort Studies, Databases, Factual, Female, Humans, Hypoxia, Infant, Intubation, Intratracheal, Male, One-Lung Ventilation, Retrospective Studies, Risk Factors, Multicenter Perioperative Outcomes Group Investigators, Anesthesiology, Clinical sciences
Abstract: BackgroundOne-lung ventilation in children remains a specialized practice with low case numbers even at tertiary centers, preventing an assessment of best practices. The authors hypothesized that certain case factors may be associated with a higher risk of intraprocedural hypoxemia in children undergoing thoracic surgery and one-lung ventilation.MethodsThe Multicenter Perioperative Outcomes database and a local quality improvement database were queried for documentation of one-lung ventilation in children 2 months to 3 yr of age inclusive between 2010 and 2020. Patients undergoing vascular or other cardiac procedures were excluded. All records were reviewed electronically for the presence of hypoxemia, oxygen saturation measured by pulse oximetry (Spo2) less than 90% for 3 min or more continuously, and severe hypoxemia, Spo2 less than 90% for 5 min or more continuously during one-lung ventilation. Records were also assessed for hypercarbia, end-tidal CO2 greater than 60 mmHg for 5 min or more or a Paco2 greater than 60 on arterial blood gas. Covariates assessed for association with these outcomes included age, weight, American Society of Anesthesiologists (Schaumburg, Illinois) Physical Status 3 or greater, duration of one-lung ventilation, preoperative Spo2 less than 98%, bronchial blocker versus endobronchial intubation, left operative side, video-assisted thoracoscopic surgery, lower tidal volume ventilation (tidal volume less than or equal to 6 ml/kg plus positive end expiratory pressure greater than or equal to 4 cm H2O for more than 80% of the duration of one-lung ventilation), and type of procedure.ResultsThree hundred six cases from 15 institutions were included for analysis. Hypoxemia and severe hypoxemia occurred in 81 of 306 (26%) patients and 56 of 306 (18%), respectively. Hypercarbia occurred in 153 of 306 (50%). Factors associated with lower risk of hypoxemia in multivariable analysis included left operative side (odds ratio, 0.45 [95% CI, 0.251 to 0.78]) and bronchial blocker use (odds ratio, 0.351 [95% CI, 0.177 to 0.67]). Additionally, use of a bronchial blocker was associated with a reduced risk of severe hypoxemia (odds ratio, 0.290 [95% CI, 0.125 to 0.62]).ConclusionsUse of a bronchial blocker was associated with a lower risk of hypoxemia in young children undergoing one-lung ventilation.Editor’s perspective
Published: 2021

32. Airway management in the paediatric difficult intubation registry: a propensity score matched analysis of outcomes over time

Author: Bruins, Benjamin, Stricker, Paul, Laverriere, Elizabeth, Lockman, Justin L., Struyk, Brian, Ward, Christopher, Nishisaki, Akira, Kodavatiganti, Ramesh, Guris, Rodrigo Daly, Sequera-Ramos, Luis, Teen, Mark, Oke, Ayodele, Hsu, Grace, Lingappan, Arul, Battles, Rhae, Bocanegra, Ashley, Goldfarb, Tally, Kiss, Edgar, Szmuk, Peter, Mireles, Sam, Murray, Andrea, Whyte, Simon, Jain, Ranu, Matuszczak, Maria, Holmes, Christopher, McCann, Alexander, Matava, Clyde, Dalesio, Nicholas, Greenberg, Robert, Lucero, Angela, Desai, Sapna, Rosander, Sondra, Samba, Sindhu, Schrock, Charles, Nykiel-Bailey, Sydney, Marsh, Jennifer, Peterson, Melissa Brooks, Lee, Amy, Bhattacharya, Somaletha, Burjek, Nicholas, Jagannathan, Narasimhan, Lardner, David, Crockett, Christy, Robetson, Sara, Patel, Jasmine, Sharma, Aarti, Templeton, Thomas, Marín, Piedad Echeverry, Pérez-Pradilla, Carolina, Singh, Neeta, Sommerfield, David, Hauser, Neil, Hesselink, Emily, Lewkowitz-Shpuntoff, Hilana, Castro, Pilar, Riveros Perez, N. Ricardo, Vega, Eduardo, González, Alejandro, Ostermann, Paola, Rubin, Kasia, Meserve, Jonathan, Lord, Charles, Lee, Angela, Valairucha, Songyos, Dalal, Priti, Tran, Thanh, Anspach, Taylor, Lee, Lisa K., Ayad, Ihab, Rehman, Mohamed, Fernandez, Allison, Zamora, Lillian, Ravula, Niroop, Shaik, Sadiq, Szolnoki, Judit, Mathew, Preethy, Yaddanapudi, Sandhya, Sen, Indu, Gupta, Aakriti, Handlogten, Kathryn, Sroka, J. Michael, Quintão, Vinícius Caldeira, Carlos, Ricardo Vieira, Leite, Fernanda, Stein, Mary Lyn, Sarmiento Argüello, Lina Andrea, Staffa, Steven J., Heunis, Julia, Egbuta, Chinyere, Flynn, Stephen G., Khan, Sabina A., Sabato, Stefano, Taicher, Brad M., Chiao, Franklin, Bosenberg, Adrian, Lee, Angela C., Adams, H. Daniel, von Ungern-Sternberg, Britta S., Park, Raymond S., Peyton, James M., Olomu, Patrick N., Hunyady, Agnes I., Garcia-Marcinkiewicz, Annery, Fiadjoe, John E., and Kovatsis, Pete G.
Published: 2024
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33. Development of a clinical practice guideline for physiotherapy management of adults invasively ventilated with community-acquired pneumonia

Author: van der Lee, Lisa, Patman, Shane, and Hill, Anne-Marie
Published: 2024
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34. Making waves: The benefits and challenges of responsibly implementing wastewater-based surveillance for rural communities

Author: Cohen, Alasdair, Vikesland, Peter, Pruden, Amy, Krometis, Leigh-Anne, Lee, Lisa M., Darling, Amanda, Yancey, Michelle, Helmick, Meagan, Singh, Rekha, Gonzalez, Raul, Meit, Michael, Degen, Marcia, and Taniuchi, Mami
Published: 2024
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35. Six Years of Quality Improvement in Pectus Excavatum Repair: Implementation of Intercostal Nerve Cryoablation and ERAS Protocols for Patients Undergoing Nuss Procedure

Author: Rook, Jordan M., Lee, Lisa K., Wagner, Justin P., Sullins, Veronica F., Lee, Steven L., Shekherdimian, Shant, DeUgarte, Daniel A., Dichter, Christine E., and Jen, Howard C.
Published: 2024
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36. f-IRL: Inverse Reinforcement Learning via State Marginal Matching

Author: Ni, Tianwei, Sikchi, Harshit, Wang, Yufei, Gupta, Tejus, Lee, Lisa, and Eysenbach, Benjamin
Subjects: Computer Science - Machine Learning, Computer Science - Robotics
Abstract: Imitation learning is well-suited for robotic tasks where it is difficult to directly program the behavior or specify a cost for optimal control. In this work, we propose a method for learning the reward function (and the corresponding policy) to match the expert state density. Our main result is the analytic gradient of any f-divergence between the agent and expert state distribution w.r.t. reward parameters. Based on the derived gradient, we present an algorithm, f-IRL, that recovers a stationary reward function from the expert density by gradient descent. We show that f-IRL can learn behaviors from a hand-designed target state density or implicitly through expert observations. Our method outperforms adversarial imitation learning methods in terms of sample efficiency and the required number of expert trajectories on IRL benchmarks. Moreover, we show that the recovered reward function can be used to quickly solve downstream tasks, and empirically demonstrate its utility on hard-to-explore tasks and for behavior transfer across changes in dynamics., Comment: The first four authors have equal contribution (orders determined by dice rolling), and the last two authors have equal advising. The paper is accepted by Conference on Robot Learning (CoRL) 2020. Project videos and code link are available at https://sites.google.com/view/f-irl/home
Published: 2020

37. A model for the fragmentation kinetics of crumpled thin sheets

Author: Andrejevic, Jovana, Lee, Lisa M., Rubinstein, Shmuel M., and Rycroft, Chris H.
Subjects: Condensed Matter - Soft Condensed Matter
Abstract: As a confined thin sheet crumples, it spontaneously segments into flat facets delimited by a network of ridges. Despite the apparent disorder of this process, statistical properties of crumpled sheets exhibit striking reproducibility. Experiments have shown that the total crease length accrues logarithmically when repeatedly compacting and unfolding a sheet of paper. Here, we offer insight to this unexpected result by exploring the correspondence between crumpling and fragmentation processes. We identify a physical model for the evolution of facet area and ridge length distributions of crumpled sheets, and propose a mechanism for re-fragmentation driven by geometric frustration. This mechanism establishes a feedback loop in which the facet size distribution informs the subsequent rate of fragmentation under repeated confinement, thereby producing a new size distribution. We then demonstrate the capacity of this model to reproduce the characteristic logarithmic scaling of total crease length, thereby supplying a missing physical basis for the observed phenomenon., Comment: 11 pages, 7 figures (+ Supplemental Materials: 15 pages, 9 figures); introduced a simpler approximation to model, key results unchanged; added references, expanded supplementary information, corrected Fig. 2 and revised Figs. 4 and 7 for clearer presentation of results
Published: 2020
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38. Weakly-Supervised Reinforcement Learning for Controllable Behavior

Author: Lee, Lisa, Eysenbach, Benjamin, Salakhutdinov, Ruslan, Gu, Shixiang Shane, and Finn, Chelsea
Subjects: Computer Science - Machine Learning, Computer Science - Robotics, Statistics - Machine Learning
Abstract: Reinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks. However, in many settings, an agent must winnow down the inconceivably large space of all possible tasks to the single task that it is currently being asked to solve. Can we instead constrain the space of tasks to those that are semantically meaningful? In this work, we introduce a framework for using weak supervision to automatically disentangle this semantically meaningful subspace of tasks from the enormous space of nonsensical "chaff" tasks. We show that this learned subspace enables efficient exploration and provides a representation that captures distance between states. On a variety of challenging, vision-based continuous control problems, our approach leads to substantial performance gains, particularly as the complexity of the environment grows., Comment: Published in NeurIPS 2020
Published: 2020

39. Author Correction: A model for the fragmentation kinetics of crumpled thin sheets.

Author: Andrejevic, Jovana, Lee, Lisa M, Rubinstein, Shmuel M, and Rycroft, Chris H
Abstract: The original version of this Article omitted DMR-1420570 from the Acknowledgements. This has now been corrected in both the PDF and HTML versions of the Article.
Published: 2021

40. Hypoxemia in School-age Children Undergoing One-lung Ventilation: A Retrospective Cohort Study from the Multicenter Perioperative Outcomes Group

Author: Templeton, T. Wesley, Krol, Bridget, Miller, Scott, Lee, Lisa K., Mathis, Michael, Vishneski, Susan R., Chatterjee, Debnath, Gupta, Ruchika, Shroeder, Rebecca A., Saha, Amit K., and Schonberger, Robert B.
Published: 2024
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41. A model for the fragmentation kinetics of crumpled thin sheets.

Author: Andrejevic, Jovana, Lee, Lisa M, Rubinstein, Shmuel M, and Rycroft, Chris H
Subjects: cond-mat.soft
Abstract: As a confined thin sheet crumples, it spontaneously segments into flat facets delimited by a network of ridges. Despite the apparent disorder of this process, statistical properties of crumpled sheets exhibit striking reproducibility. Experiments have shown that the total crease length accrues logarithmically when repeatedly compacting and unfolding a sheet of paper. Here, we offer insight to this unexpected result by exploring the correspondence between crumpling and fragmentation processes. We identify a physical model for the evolution of facet area and ridge length distributions of crumpled sheets, and propose a mechanism for re-fragmentation driven by geometric frustration. This mechanism establishes a feedback loop in which the facet size distribution informs the subsequent rate of fragmentation under repeated confinement, thereby producing a new size distribution. We then demonstrate the capacity of this model to reproduce the characteristic logarithmic scaling of total crease length, thereby supplying a missing physical basis for the observed phenomenon.
Published: 2021

42. Efficient Exploration via State Marginal Matching

Author: Lee, Lisa, Eysenbach, Benjamin, Parisotto, Emilio, Xing, Eric, Levine, Sergey, and Salakhutdinov, Ruslan
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Robotics, Statistics - Machine Learning
Abstract: Exploration is critical to a reinforcement learning agent's performance in its given environment. Prior exploration methods are often based on using heuristic auxiliary predictions to guide policy behavior, lacking a mathematically-grounded objective with clear properties. In contrast, we recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution. The target distribution is a uniform distribution in most cases, but can incorporate prior knowledge if available. In effect, SMM amortizes the cost of learning to explore in a given environment. The SMM objective can be viewed as a two-player, zero-sum game between a state density model and a parametric policy, an idea that we use to build an algorithm for optimizing the SMM objective. Using this formalism, we further demonstrate that prior work approximately maximizes the SMM objective, offering an explanation for the success of these methods. On both simulated and real-world tasks, we demonstrate that agents that directly optimize the SMM objective explore faster and adapt more quickly to new tasks as compared to prior exploration methods., Comment: Videos and code: https://sites.google.com/view/state-marginal-matching
Published: 2019

43. Difficult or impossible facemask ventilation in children with difficult tracheal intubation: a retrospective analysis of the PeDI registry

Author: Bruins, Benjamin B., Stricker, Paul, Laverriere, Elizabeth K., Lockman, Justin L., Struyk, Brian, Ward, Christopher, Nishisaki, Akira, Kodavatiganti, Ramesh, Daly Guris, Rodrigo J., Sequera-Ramos, Luis, Teen, Mark S., Oke, Ayodele, Hsu, Grace, Lingappan, Arul, Egbuta, Chinyere, Flynn, Stephen, Sarmiento, Lina, Goldfarb, Tally, Kiss, Edgar E., Olomu, Patrick N., Szmuk, Peter, Mireles, Sam, Murray, Andrea, Whyte, Simon, Jain, Ranu, Matuszczak, Maria, Hunyady, Agnes, Bosenberg, Adrian, Tham, See, Low, Daniel, Holmes, Christopher, Sabato, Stefan, Dalesio, Nicholas, Greenberg, Robert, Lucero, Angela, Reynolds, Paul, Lewis, Ian, Schrock, Charles, Nykiel-Bailey, Sydney, Starker, Elizabeth, Szolnoki, Judit, Brooks-Peterson, Melissa, Bhattacharya, Somaletha, Burjek, Nicholas E., Jagannathan, Narasimhan, Lardner, David, Watkins, Scott, Crockett, Christy, Moore, John, Robertson, Sara, Sathyamoorthy, Madhankumar, Chiao, Franklin, Patel, Jasmine, Sharma, Aarti, Echeverry Marin, Piedad, Pérez-Pradilla, Carolina, Singh, Neeta, von Ungern-Sternberg, Britta S., Sommerfield, David, Bilen-Rosas, Guelay, Lewkowitz-Shpuntoff, Hilana, Castro, Pilar, Riveros Perez, N. Ricardo, de Graaff, Jurgen C., Vega, Eduardo, González, Alejandro, Ostermann, Paola, Rubin, Kasia, Lord, Charles (Ted), Lee, Angela, Heitmiller, Eugenie, Valairucha, Songyos, Dalal, Priti, Tran, Thanh, Ayad, Ihab, Rehman, Mohamed, Fernandez, Allison, Zamora, Lillian, Ravula, Niroop, Shaik, Sadiq, Garcia-Marcinkiewicz, Annery G., Lee, Lisa K., Haydar, Bishr, Fiadjoe, John E., Matava, Clyde T., Kovatsis, Pete G., Peyton, James, Stein, Mary L., Park, Raymond, Taicher, Brad M., and Templeton, Thomas W.
Published: 2023
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44. Embodied Multimodal Multitask Learning

Author: Chaplot, Devendra Singh, Lee, Lisa, Salakhutdinov, Ruslan, Parikh, Devi, and Batra, Dhruv
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Robotics, Statistics - Machine Learning
Abstract: Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for different multimodal tasks, such as semantic goal navigation and embodied question answering. In this paper, we propose a multitask model capable of jointly learning these multimodal tasks, and transferring knowledge of words and their grounding in visual objects across the tasks. The proposed model uses a novel Dual-Attention unit to disentangle the knowledge of words in the textual representations and visual concepts in the visual representations, and align them with each other. This disentangled task-invariant alignment of representations facilitates grounding and knowledge transfer across both tasks. We show that the proposed model outperforms a range of baselines on both tasks in simulated 3D environments. We also show that this disentanglement of representations makes our model modular, interpretable, and allows for transfer to instructions containing new words by leveraging object detectors., Comment: See https://devendrachaplot.github.io/projects/EMML for demo videos
Published: 2019

45. Addressing Ethical Challenges in US-Based HIV Phylogenetic Research

Author: Dawson, Liza, Benbow, Nanette, Fletcher, Faith E, Kassaye, Seble, Killelea, Amy, Latham, Stephen R, Lee, Lisa M, Leitner, Thomas, Little, Susan J, Mehta, Sanjay R, Martinez, Omar, Minalga, Brian, Poon, Art, Rennie, Stuart, Sugarman, Jeremy, Sweeney, Patricia, Torian, Lucia V, and Wertheim, Joel O
Subjects: Public Health, Biomedical and Clinical Sciences, Clinical Sciences, Health Sciences, HIV/AIDS, Clinical Research, Sexually Transmitted Infections, Prevention, Infectious Diseases, Aetiology, 8.3 Policy, ethics, and research governance, 2.4 Surveillance and distribution, Health and social care services research, Infection, Good Health and Well Being, Advisory Committees, Biomedical Research, Community Participation, Computer Security, Confidentiality, HIV, HIV Infections, Humans, Information Dissemination, National Institutes of Health (U.S.), Phylogeny, Public Health Surveillance, Research Design, United States, ethics, phylogenetics, public health, Biological Sciences, Medical and Health Sciences, Microbiology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract: In recent years, phylogenetic analysis of HIV sequence data has been used in research studies to investigate transmission patterns between individuals and groups, including analysis of data from HIV prevention clinical trials, in molecular epidemiology, and in public health surveillance programs. Phylogenetic analysis can provide valuable information to inform HIV prevention efforts, but it also has risks, including stigma and marginalization of groups, or potential identification of HIV transmission between individuals. In response to these concerns, an interdisciplinary working group was assembled to address ethical challenges in US-based HIV phylogenetic research. The working group developed recommendations regarding (1) study design; (2) data security, access, and sharing; (3) legal issues; (4) community engagement; and (5) communication and dissemination. The working group also identified areas for future research and scholarship to promote ethical conduct of HIV phylogenetic research.
Published: 2020

46. The Role of MicroRNAs in Acute Respiratory Distress Syndrome and Sepsis, From Targets to Therapies: A Narrative Review.

Author: Lee, Lisa K, Medzikovic, Lejla, Eghbali, Mansoureh, Eltzschig, Holger K, and Yuan, Xiaoyi
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Lung, Hematology, Genetics, Rare Diseases, Biotechnology, Acute Respiratory Distress Syndrome, Sepsis, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Respiratory, Animals, Drug Delivery Systems, Humans, MicroRNAs, Respiratory Distress Syndrome, Neurosciences, Anesthesiology, Clinical sciences
Abstract: Acute respiratory distress syndrome (ARDS) is a significant cause of morbidity and mortality in the intensive care unit (ICU) and is characterized by lung epithelial and endothelial cell injury, with increased permeability of the alveolar-capillary membrane, leading to pulmonary edema, severe hypoxia, and difficulty with ventilation. The most common cause of ARDS is sepsis, and currently, treatment of ARDS and sepsis has consisted mostly of supportive care because targeted therapies have largely been unsuccessful. The molecular mechanisms behind ARDS remain elusive. Recently, a number of microRNAs (miRNAs) identified through high-throughput screening studies in ARDS patients and preclinical animal models have suggested a role for miRNA in the pathophysiology of ARDS. miRNAs are small noncoding RNAs ranging from 18 to 24 nucleotides that regulate gene expression via inhibition of the target mRNA translation or by targeting complementary mRNA for early degradation. Unsurprisingly, some miRNAs that are differentially expressed in ARDS overlap with those important in sepsis. In addition, circulatory miRNA may be useful as biomarkers or as targets for pharmacologic therapy. This can be revolutionary in a syndrome that has neither a measurable indicator of the disease nor a targeted therapy. While there are currently no miRNA-based therapies targeted for ARDS, therapies targeting miRNA have reached phase II clinical trials for the treatment of a wide range of diseases. Further studies may yield a unique miRNA profile pattern that serves as a biomarker or as targets for miRNA-based pharmacologic therapy. In this review, we discuss miRNAs that have been found to play a role in ARDS and sepsis, the potential mechanism of how particular miRNAs may contribute to the pathophysiology of ARDS, and strategies for pharmacologically targeting miRNA as therapy.
Published: 2020

47. Hunting down the dominating subclone of cancer stem cells as a potential new therapeutic target in multiple myeloma: An artificial intelligence perspective.

Author: Lee, Lisa X and Li, Shengwen Calvin
Subjects: Artificial intelligence medicine, Cancer stem cells, Multiple myeloma, Single cells, Single-cell transcriptome, Subclonal evolution, Systemic tracking of single-cell landscape, Human Genome, Hematology, Cancer, Rare Diseases, Genetics, Good Health and Well Being
Abstract: The development of single-cell subclones, which can rapidly switch from dormant to dominant subclones, occur in the natural pathophysiology of multiple myeloma (MM) but is often "pressed" by the standard treatment of MM. These emerging subclones present a challenge, providing reservoirs for chemoresistant mutations. Technological advancement is required to track MM subclonal changes, as understanding MM's mechanism of evolution at the cellular level can prompt the development of new targeted ways of treating this disease. Current methods to study the evolution of subclones in MM rely on technologies capable of phenotypically and genotypically characterizing plasma cells, which include immunohistochemistry, flow cytometry, or cytogenetics. Still, all of these technologies may be limited by the sensitivity for picking up rare events. In contrast, more incisive methods such as RNA sequencing, comparative genomic hybridization, or whole-genome sequencing are not yet commonly used in clinical practice. Here we introduce the epidemiological diagnosis and prognosis of MM and review current methods for evaluating MM subclone evolution, such as minimal residual disease/multiparametric flow cytometry/next-generation sequencing, and their respective advantages and disadvantages. In addition, we propose our new single-cell method of evaluation to understand MM's mechanism of evolution at the molecular and cellular level and to prompt the development of new targeted ways of treating this disease, which has a broad prospect.
Published: 2020

48. Pediatric Airway Management in Coronavirus Disease 2019 Patients: Consensus Guidelines From the Society for Pediatric Anesthesia’s Pediatric Difficult Intubation Collaborative and the Canadian Pediatric Anesthesia Society

Author: Matava, Clyde T, Kovatsis, Pete G, Lee, Jennifer K, Castro, Pilar, Denning, Simon, Yu, Julie, Park, Raymond, Lockman, Justin L, Von Ungern-Sternberg, Britta, Sabato, Stefano, Lee, Lisa K, Ayad, Ihab, Mireles, Sam, Lardner, David, Whyte, Simon, Szolnoki, Judit, Jagannathan, Narasimhan, Thompson, Nicole, Stein, Mary Lyn, Dalesio, Nicholas, Greenberg, Robert, McCloskey, John, Peyton, James, Evans, Faye, Haydar, Bishr, Reynolds, Paul, Chiao, Franklin, Taicher, Brad, Templeton, Thomas, Bhalla, Tarun, Raman, Vidya T, Garcia-Marcinkiewicz, Annery, Gálvez, Jorge, Tan, Jonathan, Rehman, Mohamed, Crockett, Christy, Olomu, Patrick, Szmuk, Peter, Glover, Chris, Matuszczak, Maria, Galvez, Ignacio, Hunyady, Agnes, Polaner, David, Gooden, Cheryl, Hsu, Grace, Gumaney, Harshad, Pérez-Pradilla, Caroline, Kiss, Edgar E, Theroux, Mary C, Lau, Jennifer, Asaf, Saeedah, Ingelmo, Pablo, Engelhardt, Thomas, Hervías, Mónica, Greenwood, Eric, Javia, Luv, Disma, Nicola, Yaster, Myron, and Fiadjoe, John E
Subjects: Patient Safety, Lung, Pediatric, Clinical Research, Good Health and Well Being, Adolescent, Airway Management, Anesthesia, Anesthesiology, COVID-19, Child, Child, Preschool, Consensus, Coronavirus Infections, Guidelines as Topic, Humans, Infant, Infant, Newborn, Infection Control, Infectious Disease Transmission, Patient-to-Professional, Intubation, Intratracheal, Pandemics, Pediatrics, Pneumonia, Viral, PeDI-Collaborative, Clinical Sciences, Neurosciences
Abstract: The severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019 [COVID-19]) pandemic has challenged medical systems and clinicians globally to unforeseen levels. Rapid spread of COVID-19 has forced clinicians to care for patients with a highly contagious disease without evidence-based guidelines. Using a virtual modified nominal group technique, the Pediatric Difficult Intubation Collaborative (PeDI-C), which currently includes 35 hospitals from 6 countries, generated consensus guidelines on airway management in pediatric anesthesia based on expert opinion and early data about the disease. PeDI-C identified overarching goals during care, including minimizing aerosolized respiratory secretions, minimizing the number of clinicians in contact with a patient, and recognizing that undiagnosed asymptomatic patients may shed the virus and infect health care workers. Recommendations include administering anxiolytic medications, intravenous anesthetic inductions, tracheal intubation using video laryngoscopes and cuffed tracheal tubes, use of in-line suction catheters, and modifying workflow to recover patients from anesthesia in the operating room. Importantly, PeDI-C recommends that anesthesiologists consider using appropriate personal protective equipment when performing aerosol-generating medical procedures in asymptomatic children, in addition to known or suspected children with COVID-19. Airway procedures should be done in negative pressure rooms when available. Adequate time should be allowed for operating room cleaning and air filtration between surgical cases. Research using rigorous study designs is urgently needed to inform safe practices during the COVID-19 pandemic. Until further information is available, PeDI-C advises that clinicians consider these guidelines to enhance the safety of health care workers during airway management when performing aerosol-generating medical procedures. These guidelines have been endorsed by the Society for Pediatric Anesthesia and the Canadian Pediatric Anesthesia Society.
Published: 2020

49. Pediatric Airway Management in COVID-19 Patients: Consensus Guidelines From the Society for Pediatric Anesthesia's Pediatric Difficult Intubation Collaborative and the Canadian Pediatric Anesthesia Society.

Author: Matava, Clyde T, Kovatsis, Pete G, Lee, Jennifer K, Castro, Pilar, Denning, Simon, Yu, Julie, Park, Raymond, Lockman, Justin L, Von Ungern-Sternberg, Britta, Sabato, Stefano, Lee, Lisa K, Ayad, Ihab, Mireles, Sam, Lardner, David, Whyte, Simon, Szolnoki, Judit, Jagannathan, Narasimhan, Thompson, Nicole, Stein, Mary Lyn, Dalesio, Nicholas, Greenberg, Robert, McCloskey, John, Peyton, James, Evans, Faye, Haydar, Bishr, Reynolds, Paul, Chiao, Franklin, Taicher, Brad, Templeton, Thomas, Bhalla, Tarun, Raman, Vidya T, Garcia-Marcinkiewicz, Annery, Gálvez, Jorge, Tan, Jonathan, Rehman, Mohamed, Crockett, Christy, Olomu, Patrick, Szmuk, Peter, Glover, Chris, Matuszczak, Maria, Galvez, Ignacio, Hunyady, Agnes, Polaner, David, Gooden, Cheryl, Hsu, Grace, Gumaney, Harshad, Pérez-Pradilla, Caroline, Kiss, Edgar E, Theroux, Mary C, Lau, Jennifer, Asaf, Saeedah, Ingelmo, Pablo, Engelhardt, Thomas, Hervías, Mónica, Greenwood, Eric, Javia, Luv, Disma, Nicola, Yaster, Myron, Fiadjoe, John E, and PeDI-Collaborative
Subjects: PeDI-Collaborative, Humans, Pneumonia, Viral, Coronavirus Infections, Anesthesia, Intubation, Intratracheal, Consensus, Anesthesiology, Pediatrics, Infection Control, Adolescent, Child, Child, Preschool, Infant, Infant, Newborn, Guidelines as Topic, Infectious Disease Transmission, Patient-to-Professional, Airway Management, Pandemics, Pneumonia, Viral, Intubation, Intratracheal, Preschool, Newborn, Infectious Disease Transmission, Patient-to-Professional, Clinical Sciences, Neurosciences
Abstract: The severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019 [COVID-19]) pandemic has challenged medical systems and clinicians globally to unforeseen levels. Rapid spread of COVID-19 has forced clinicians to care for patients with a highly contagious disease without evidence-based guidelines. Using a virtual modified nominal group technique, the Pediatric Difficult Intubation Collaborative (PeDI-C), which currently includes 35 hospitals from 6 countries, generated consensus guidelines on airway management in pediatric anesthesia based on expert opinion and early data about the disease. PeDI-C identified overarching goals during care, including minimizing aerosolized respiratory secretions, minimizing the number of clinicians in contact with a patient, and recognizing that undiagnosed asymptomatic patients may shed the virus and infect health care workers. Recommendations include administering anxiolytic medications, intravenous anesthetic inductions, tracheal intubation using video laryngoscopes and cuffed tracheal tubes, use of in-line suction catheters, and modifying workflow to recover patients from anesthesia in the operating room. Importantly, PeDI-C recommends that anesthesiologists consider using appropriate personal protective equipment when performing aerosol-generating medical procedures in asymptomatic children, in addition to known or suspected children with COVID-19. Airway procedures should be done in negative pressure rooms when available. Adequate time should be allowed for operating room cleaning and air filtration between surgical cases. Research using rigorous study designs is urgently needed to inform safe practices during the COVID-19 pandemic. Until further information is available, PeDI-C advises that clinicians consider these guidelines to enhance the safety of health care workers during airway management when performing aerosol-generating medical procedures. These guidelines have been endorsed by the Society for Pediatric Anesthesia and the Canadian Pediatric Anesthesia Society.
Published: 2020

50. Involvement of Low‐Density Lipoprotein Receptor in the Pathogenesis of Pulmonary Hypertension

Author: Umar, Soban, Ruffenach, Gregoire, Moazeni, Shayan, Vaillancourt, Mylene, Hong, Jason, Cunningham, Christine, Cao, Nancy, Navab, Sara, Sarji, Shervin, Li, Min, Lee, Lisa, Fishbein, Greg, Ardehali, Abbas, Navab, Mohamad, Reddy, Srinivasa T, and Eghbali, Mansoureh
Subjects: Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Cardiovascular, Lung, Heart Disease, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, Animals, Apolipoprotein A-I, CD36 Antigens, Case-Control Studies, Cells, Cultured, Disease Models, Animal, Fibrosis, Hemodynamics, Humans, Hypertension, Pulmonary, Lipoproteins, LDL, Male, Mice, Knockout, Pulmonary Artery, Receptors, LDL, Signal Transduction, Vascular Remodeling, Ventricular Dysfunction, Left, Ventricular Dysfunction, Right, low-density lipoprotein receptor, oxidized lipids, oxidized low-density lipoprotein, pulmonary hypertension, Western diet, low‐density lipoprotein receptor, oxidized low‐density lipoprotein, Cardiorespiratory Medicine and Haematology, Cardiovascular medicine and haematology
Abstract: Background Recently, we and others have reported a causal role for oxidized lipids in the pathogenesis of pulmonary hypertension (PH). However, the role of low-density lipoprotein receptor (LDL-R) in PH is not known. Methods and Results We examined the role of LDL-R in the development of PH and determined the efficacy of high-density lipoprotein mimetic peptide 4F in mitigating PH. Explanted human lungs and plasma from patients with PH and control subjects were analyzed for gene expression, histological characteristics, and lipoprotein oxidation. Male LDL-R null (LDL-R knockout) mice (12-15 months old) were fed chow, Western diet (WD), WD with 4F, and WD with scramble peptide for 12 weeks. Serial echocardiography, cardiac catheterization, oxidized LDL assay, real-time quantitative reverse transcription-polymerase chain reaction, and histological analysis were performed. The effect of LDL-R knockdown and oxidized LDL on human pulmonary artery smooth muscle cell proliferation was assessed in vitro. LDL-R and CD36 expression levels were significantly downregulated in the lungs of patients with PH. Patients with PH also had increased lung lipid deposits, oxidized LDL, E06 immunoreactivity, and plasma oxidized LDL/LDL ratio. LDL-R knockout mice on WD developed PH, right ventricular hypertrophy, right ventricular dysfunction, pulmonary vascular remodeling, fibrosis, and lipid deposition in lungs, aortic atherosclerosis, and left ventricular dysfunction, which were prevented by 4F. Interestingly, PH in WD group preceded left ventricular dysfunction. Oxidized LDL or LDL-R knockdown significantly increased proliferation of human pulmonary artery smooth muscle cells in vitro. Conclusions Human PH is associated with decreased LDL-R in lungs and increased oxidized LDL in lungs and plasma. WD-fed LDL-R knockout mice develop PH and right ventricular dysfunction, implicating a role for LDL-R and oxidized lipids in PH.
Published: 2020

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