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54. A mature quasar at cosmic dawn revealed by JWST rest-frame infrared spectroscopy

Author

Bosman, Sarah E. I., Álvarez-Márquez, Javier, Colina, Luis, Walter, Fabian, Alonso-Herrero, Almudena, Ward, Martin J., Östlin, Göran, Greve, Thomas R., Wright, Gillian, Bik, Arjan, Boogaard, Leindert, Caputi, Karina, Costantin, Luca, Eckart, Andreas, García-Marín, Macarena, Gillman, Steven, Hjorth, Jens, Iani, Edoardo, Ilbert, Olivier, Jermann, Iris, Labiano, Alvaro, Langeroodi, Danial, Peißker, Florian, Rinaldi, Pierluigi, Topinka, Martin, van der Werf, Paul, Güdel, Manuel, Henning, Thomas, Lagage, Pierre-Olivier, Ray, Tom P., van Dishoeck, Ewine F., and Vandenbussche, Bart

Published
2024
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63. Pregnancy-adapted YEARS Algorithm: A Retrospective Analysis

Author

Mileto, Alden, Rossi, Gina, Krouse, Benjamin, Rinaldi, Robert, Ma, Julia, Willner, Keith, and Lisbon, David

Subjects
pulmonary embolism, YEARS criteria, pregnant patients
Abstract

Introduction: Pulmonary embolism (PE) is an imperative diagnosis to make given its associated morbidity. There is no current consensus in the initial workup of pregnant patients suspected of a PE. Prospective studies have been conducted in Europe using a pregnancy-adapted YEARS algorithm, which showed safe reductions in computed tomography pulmonary angiography (CTPA) imaging in pregnant patients suspected of PE. Our objective in this study was 1) to measure the potential avoidance of CTPA use in pregnant patients if the pregnancy-adapted YEARS algorithm had been applied and 2) to serve as an external validation study of the use of this algorithm in the United States.Methods: This study was a single-system retrospective chart analysis. Criteria for inclusion in the cohort consisted of keywords: pregnant; older than 18; chief complaints of shortness of breath, chest pain, tachycardia, hemoptysis, deep vein thromboembolism (DVT), and D-dimer—from January 1, 2019– May 31,2022. We then analyzed this cohort retrospectively using the pregnancy-adapted YEARS algorithm, which includes clinical signs of a DVT, hemoptysis, and PE as the most likely diagnosis with a D-dimer assay. Patients within the cohort were then subdivided into two categories: aligned with the YEARS algorithm, or not aligned with the YEARS algorithm. Patients who did not receive a CTPA were analyzed for a subsequent diagnosis of a PE or DVT within 30 days.Results: A total of 74 pregnant patients were included in this study. There was a PE prevalence of 2.7% (two patients). Of the 36 patients who did not require imaging by the algorithm, seven CTPA were performed. Of the patients who did not receive an initial CTPA, zero were diagnosed with PE or DVT within a 30-day follow-up. In total, 85.1% of all the patients in this study were treated in concordance with the pregnancy-adapted YEARS algorithm.Conclusion: The use of the pregnancy-adapted YEARS algorithm could have resulted in decreased utilization of CTPA in the workup of PE in pregnant patients, and the algorithm showed similar reductions compared to prospective studies done in Europe. The pregnancy-adapted YEARS algorithm was also shown to be similar to the clinical rationale used by clinicians in the evaluation of pregnant patients, which indicates its potential for widespread acceptance into clinical practice.

Published
2024

64. 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, Joughin, Jonah, Filonov, Egor, Kępa, Tomasz, Eldawy, Yomna, Lim, Jiawern, Rishi, Rahul, Badiezadegan, Shirin, Bos, Taylor, Chang, Jerry, Jain, Sanil, Padmanabhan, Sri Gayatri Sundara, Puttagunta, Subha, Krishna, Kalpesh, Baker, Leslie, Kalb, Norbert, Bedapudi, Vamsi, Kurzrok, Adam, Lei, Shuntong, Yu, Anthony, Litvin, Oren, Zhou, Xiang, Wu, Zhichun, Sobell, Sam, Siciliano, Andrea, Papir, Alan, Neale, Robby, Bragagnolo, Jonas, Toor, Tej, Chen, Tina, Anklin, Valentin, Wang, Feiran, Feng, Richie, Gholami, Milad, Ling, Kevin, Liu, Lijuan, Walter, Jules, Moghaddam, Hamid, Kishore, Arun, Adamek, Jakub, Mercado, Tyler, Mallinson, Jonathan, Wandekar, Siddhinita, Cagle, Stephen, Ofek, Eran, Garrido, Guillermo, Lombriser, Clemens, Mukha, Maksim, Sun, Botu, Mohammad, Hafeezul Rahman, Matak, Josip, Qian, Yadi, Peswani, Vikas, Janus, Pawel, Yuan, Quan, Schelin, Leif, David, Oana, Garg, Ankur, He, Yifan, Duzhyi, Oleksii, Älgmyr, Anton, Lottaz, Timothée, Li, Qi, Yadav, Vikas, Xu, Luyao, Chinien, Alex, 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, Bacon, Geoff, Greene, David, Mirylenka, Daniil, Zhou, Chen, Sarvana, Obaid, Goyal, Abhimanyu, Andermatt, Samuel, Siegler, Patrick, Horn, Ben, Israel, Assaf, Pongetti, Francesco, Chen, Chih-Wei "Louis", Selvatici, Marco, Silva, Pedro, Wang, Kathie, Tolins, Jackson, Guu, Kelvin, Yogev, Roey, Cai, Xiaochen, Agostini, Alessandro, Shah, Maulik, Nguyen, Hung, Donnaile, Noah Ó, Pereira, Sébastien, Friso, Linda, Stambler, Adam, Kuang, Chenkai, Romanikhin, Yan, Geller, Mark, Yan, ZJ, Jang, Kane, Lee, Cheng-Chun, Fica, Wojciech, Malmi, Eric, Tan, Qijun, Banica, Dan, Balle, Daniel, Pham, Ryan, Huang, Yanping, Avram, Diana, Shi, Hongzhi, Singh, Jasjot, Hidey, Chris, Ahuja, Niharika, Saxena, Pranab, Dooley, Dan, Potharaju, Srividya Pranavi, O'Neill, Eileen, Gokulchandran, Anand, Foley, Ryan, Zhao, Kai, Dusenberry, Mike, Liu, Yuan, Mehta, Pulkit, Kotikalapudi, Ragha, Safranek-Shrader, Chalence, Goodman, Andrew, Kessinger, Joshua, Globen, Eran, Kolhar, Prateek, Gorgolewski, Chris, Ibrahim, Ali, Song, Yang, Eichenbaum, Ali, Brovelli, Thomas, Potluri, Sahitya, Lahoti, Preethi, Baetu, Cip, Ghorbani, Ali, Chen, Charles, Crawford, Andy, Pal, Shalini, Sridhar, Mukund, Gurita, Petru, Mujika, Asier, Petrovski, Igor, Cedoz, Pierre-Louis, Li, Chenmei, Chen, Shiyuan, Santo, Niccolò Dal, Goyal, Siddharth, Punjabi, Jitesh, Kappaganthu, Karthik, Kwak, Chester, LV, Pallavi, Velury, Sarmishta, Choudhury, Himadri, Hall, Jamie, Shah, Premal, Figueira, Ricardo, Thomas, Matt, Lu, Minjie, Zhou, Ting, Kumar, Chintu, Jurdi, Thomas, Chikkerur, Sharat, Ma, Yenai, Yu, Adams, Kwak, Soo, Ähdel, Victor, Rajayogam, Sujeevan, Choma, Travis, Liu, Fei, Barua, Aditya, Ji, Colin, Park, Ji Ho, Hellendoorn, Vincent, Bailey, Alex, Bilal, Taylan, Zhou, Huanjie, Khatir, Mehrdad, Sutton, Charles, Rzadkowski, Wojciech, Macintosh, Fiona, Shagin, Konstantin, Medina, Paul, Liang, Chen, Zhou, Jinjing, Shah, Pararth, Bi, Yingying, Dankovics, Attila, Banga, Shipra, Lehmann, Sabine, Bredesen, Marissa, Lin, Zifan, Hoffmann, John Eric, Lai, Jonathan, Chung, Raynald, Yang, Kai, Balani, Nihal, Bražinskas, Arthur, Sozanschi, Andrei, Hayes, Matthew, Alcalde, Héctor Fernández, Makarov, Peter, Chen, Will, Stella, Antonio, Snijders, Liselotte, Mandl, Michael, Kärrman, Ante, Nowak, Paweł, Wu, Xinyi, Dyck, Alex, Vaidyanathan, Krishnan, R, Raghavender, Mallet, Jessica, Rudominer, Mitch, Johnston, Eric, Mittal, Sushil, Udathu, Akhil, Christensen, Janara, Verma, Vishal, Irving, Zach, Santucci, Andreas, Elsayed, Gamaleldin, Davoodi, Elnaz, Georgiev, Marin, Tenney, Ian, Hua, Nan, Cideron, Geoffrey, Leurent, Edouard, Alnahlawi, Mahmoud, Georgescu, Ionut, Wei, Nan, Zheng, Ivy, Scandinaro, Dylan, Jiang, Heinrich, Snoek, Jasper, Sundararajan, Mukund, Wang, Xuezhi, Ontiveros, Zack, Karo, Itay, Cole, Jeremy, Rajashekhar, Vinu, Tumeh, Lara, Ben-David, Eyal, Jain, Rishub, Uesato, Jonathan, Datta, Romina, Bunyan, Oskar, Wu, Shimu, Zhang, John, Stanczyk, Piotr, Zhang, Ye, Steiner, David, Naskar, Subhajit, Azzam, Michael, Johnson, 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

65. Stealth dark matter spectrum using LapH and Irreps

Author

Brower, Richard C., Culver, Christopher, Cushman, Kimmy K., Fleming, George T., Hasenfratz, Anna, Howarth, Dean, Ingoldby, James, Jin, Xiao Yong, Kribs, Graham D., Meyer, Aaron S., Neil, Ethan T., Osborn, James C., Owen, Evan, Park, Sungwoo, Rebbi, Claudio, Rinaldi, Enrico, Schaich, David, Vranas, Pavlos, Weinberg, Evan, and Witzel, Oliver

Subjects
High Energy Physics - Lattice, High Energy Physics - Phenomenology
Abstract

We present non-perturbative lattice calculations of the low-lying meson and baryon spectrum of the SU(4) gauge theory with fundamental fermion constituents. This theory is one instance of stealth dark matter, a class of strongly coupled theories, where the lowest mass stable baryon is the dark matter candidate. This work constitutes the first milestone in the program to study stealth dark matter self-interactions. Here, we focus on reducing excited state contamination in the single baryon channel by applying the Laplacian Heaviside method, as well as projecting our baryon operators onto the irreducible representations of the octahedral group. We compare our resulting spectrum to previous work involving Gaussian smeared non-projected operators and find good agreement with reduced statistical uncertainties. We also present the spectrum of the low-lying odd-parity baryons for the first time., Comment: 19 pages, 9 figures

Published
2023

66. Clumpy star formation and an obscured nuclear starburst in the luminous dusty z=4 galaxy GN20 seen by MIRI/JWST

Author

Bik, A., Álvarez-Márquez, J., Colina, L., Gómez, A. Crespo, Peissker, F., Walter, F., Boogaard, L. A., Östlin, G., Greve, T. R., Wright, G., Alonso-Herrero, A., Caputi, K. I., Costantin, L., Eckart, A., Gillman, S., Hjorth, J., Iani, E., Jermann, I., Labiano, A., Langeroodi, D., Melinder, J., Pérez-González, P. G., Pye, J. P., Rinaldi, P., Tikkanen, T., van der Werf, P., Güdel, M., Henning, Th., Lagage, P. O., Ray, T., and van Dishoeck, E. F.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Dusty star-forming galaxies emit most of their light at far-IR to mm wavelengths as their star formation is highly obscured. Far-IR and mm observations have revealed their dust, neutral and molecular gas properties. The sensitivity of JWST at rest-frame optical and near-infrared wavelengths now allows the study of the stellar and ionized gas content. We investigate the spatially resolved distribution and kinematics of the ionized gas in GN20, a dusty star forming galaxy at $z$=4.0548. We present deep MIRI/MRS integral field spectroscopy of the near-infrared rest-frame emission of GN20. We detect spatially resolved \paa, out to a radius of 6 kpc, distributed in a clumpy morphology. The star formation rate derived from \paa\ (144 $\pm$ 9 \msunperyear) is only 7.7 $\pm 0.5 $\% of the infrared star formation rate (1860 $\pm$ 90 \msunperyear). We attribute this to very high extinction (A$_V$ = 17.2 $\pm$ 0.4 mag, or A$_{V,mixed}$ = 44 $\pm$ 3 mag), especially in the nucleus of GN20, where only faint \paa\ is detected, suggesting a deeply buried starburst. We identify four, spatially unresolved, clumps in the \paa\ emission. Based on the double peaked \paa\ profile we find that each clump consist of at least two sub-clumps. We find mass upper limits consistent with them being formed in a gravitationally unstable gaseous disk. The UV bright region of GN20 does not have any detected \paa\ emission, suggesting an age of more than 10 Myrs for this region of the galaxy. From the rotation profile of \paa\ we conclude that the gas kinematics are rotationally dominated and the $v_{rot}/\sigma_{m} = 3.8 \pm 1.4$ is similar to low-redshift LIRGs. We speculate that the clumps seen in GN20 could contribute to building up the inner disk and bulge of GN20., Comment: 13 pages, 7 figures, accepted for publication in A&A

Published
2023
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67. A high incidence of dusty H$\alpha$ emitters at $z>3$ among UltraVISTA dropout galaxies in COSMOS revealed by JWST

Author

van Mierlo, Sophie E., Caputi, Karina I., Ashby, Matthew L. N., Kokorev, Vasily, Navarro-Carrera, Rafael, and Rinaldi, Pierluigi

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We have characterized 26 Spitzer/IRAC-selected sources from the SMUVS program that are undetected in the UltraVISTA DR5 H- and/or Ks-band images, covering 94 square arcmin of the COSMOS field which have deep multiwavelength JWST photometry. We analyzed the JWST/NIRCam imaging from the PRIMER survey and ancillary HST data to reveal the properties of these galaxies from spectral energy distribution (SED) fitting. We find that the majority of these galaxies are detected by NIRCam at <2 micron, with only four remaining as near-infrared dropouts in the deeper JWST images. Our results indicate that the UltraVISTA dropouts candidates are primarily located at z>3 and are characterized by high dust extinctions, with a typical color excess E(B-V) = 0.5 pm 0.3 and stellar mass log(M*/Msun) = 9.5 pm 1.0. Remarkably, ~75% of these sources show a flux enhancement between the observed photometry and modeled continuum SED that can be attributed to Halpha emission in the corresponding NIRCam bands. The derived (Halpha+ N[II] + S[II]) rest-frame equivalent widths and Halpha star formation rates (SFRs) span values ~100-2200 A and ~5-375 Msun/yr, respectively. The location of these sources on the SFR-M* plane indicates that 35% of them are starbursts, 40% are main-sequence galaxies and the remaining 25% are located in the star-formation valley. Our sample includes one active galactic nucleus and six submillimeter sources, as revealed from ancillary X-ray and submillimeter photometry. The high dust extinctions combined with the flux boosting from Halpha emission explain why these sources are relatively bright Spitzer galaxies and yet unidentified in the ultradeep UltraVISTA near-infrared images., Comment: 27 pages, 14 figures, 3 tables; final version accepted for publication in ApJ

Published
2023

68. Millimeter Wave Thin-Film Bulk Acoustic Resonator in Sputtered Scandium Aluminum Nitride Using Platinum Electrodes

Author

Cho, Sinwoo, Barrera, Omar, Simeoni, Pietro, Wang, Ellie Y., Kramer, Jack, Chulukhadze, Vakhtang, Campbell, Joshua, Rinaldi, Matteo, and Lu, Ruochen

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This work describes sputtered scandium aluminum nitride (ScAlN) thin-film bulk acoustic resonators (FBAR) at millimeter wave (mmWave) with high quality factor (Q) using platinum (Pt) electrodes. FBARs with combinations of Pt and aluminum (Al) electrodes, i.e., Al top Al bottom, Pt top Al bottom, Al top Pt bottom, and Pt top Pt bottom, are built to study the impact of electrodes on mmWave FBARs. The demonstrated FBAR with Pt top and bottom electrodes achieve electromechanical coupling (k2) of 4.0% and Q of 116 for the first-order symmetric (S1) mode at 13.7 GHz, and k2 of 1.8% and Q of 94 for third-order symmetric (S3) mode at 61.6 GHz. Through these results, we confirmed that even in the frequency band of approximately 60 GHz, ScAlN FBAR can achieve a Q factor approaching 100 with optimized fabrication and acoustic/EM design. Further development calls for stacks with better quality in piezoelectric and metallic layers., Comment: 4 pages, 7 figures, accepted by IEEE MEMS 2024

Published
2023
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69. MIDIS: The Relation between Strong (Hb+[OIII]) Emission, Star Formation and Burstiness Around the Epoch of Reionization

Author

Caputi, Karina I., Rinaldi, Pierluigi, Iani, Edoardo, Pérez-González, Pablo G., Ostlin, Göran, Colina, Luis, Greve, Thomas R., Nørgaard-Nielsen, Hans-Ulrik, Wright, Gillian S., Alvarez-Márquez, Javier, Eckart, Andreas, Hjorth, Jens, Labiano, Alvaro, Fèvre, Olivier Le, Walter, Fabian, van der Werf, Paul, Boogaard, Leindert, Costantin, Luca, Crespo-Gómez, Alejandro, Gillman, Steven, Jermann, Iris, Langeroodi, Danial, Melinder, Jens, Peissker, Florian, Güdel, Manuel, Henning, Thomas, Lagage, Pierre-Olivier, and Ray, Thomas P.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We investigate the properties of strong (Hb+[OIII]) emitters before and after the end of the Epoch of Reionization from z=8 to z=5.5. We make use of ultra-deep JWST/NIRCam imaging in the Parallel Field of the MIRI Deep Imaging Survey (MIDIS) in the Hubble eXtreme Deep Field (P2-XDF), in order to select prominent (Hb+[OIII]) emitters (with rest EW_0 > 100 Angstroms) at z=5.5-7, based on their flux density enhancement in the F356W band with respect to the spectral energy distribution continuum. We complement our selection with other (Hb+[OIII]) emitters from the literature at similar and higher (z=7-8) redshifts. We find (non-independent) anti-correlations between EW_0(Hb+[OIII]) and both galaxy stellar mass and age, in agreement with previous studies, and a positive correlation with specific star formation rate (sSFR). On the SFR-M* plane, the (Hb+[OIII]) emitters populate both the star-formation main sequence and the starburst region, which become indistinguishable at low stellar masses (log10(M*) < 7.5). We find tentative evidence for a non-monotonic relation between EW_0(Hb+[OIII]) and SFR, such that both parameters correlate with each other at SFR > 1 Msun/yr, while the correlation flattens out at lower SFRs. This suggests that low metallicities producing high EW_0(Hb+[OIII]) could be important at low SFR values. Interestingly, the properties of the strong emitters and other galaxies (33% and 67% of our z=5.5-7 sample, respectively) are similar, including, in many cases, high sSFR. Therefore, it is crucial to consider both emitters and non-emitters to obtain a complete picture of the cosmic star formation activity around the Epoch of Reionization., Comment: 14 pages, 8 figures. Accepted for publication at the ApJ. Version v3 includes minor update to match journal version

Published
2023

70. HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation

Author

Tommasino, Cristian, Russo, Cristiano, Rinaldi, Antonio Maria, and Ciompi, Francesco

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

We present HoVer-UNet, an approach to distill the knowledge of the multi-branch HoVerNet framework for nuclei instance segmentation and classification in histopathology. We propose a compact, streamlined single UNet network with a Mix Vision Transformer backbone, and equip it with a custom loss function to optimally encode the distilled knowledge of HoVerNet, reducing computational requirements without compromising performances. We show that our model achieved results comparable to HoVerNet on the public PanNuke and Consep datasets with a three-fold reduction in inference time. We make the code of our model publicly available at https://github.com/DIAGNijmegen/HoVer-UNet., Comment: 4 pages, 2 figures, submitted to ISBI 2024

Published
2023

71. First-order bulk transitions in large-$N$ lattice Yang--Mills theories using the density of states

Author

Springer, Felix, Schaich, David, and Rinaldi, Enrico

Subjects
High Energy Physics - Lattice
Abstract

We use the Logarithmic Linear Relaxation (LLR) density of states algorithm to study the bulk phase transitions of pure-gauge SU($N$) lattice Yang--Mills theories with $4 \leq N \leq 8$. This approach avoids super-critical slowing down at such transitions, which poses a problem for traditional importance sampling Monte-Carlo methods. We analyse the effect of different updating strategies within the LLR algorithm, different reconstruction techniques of the density of states and different lattice volumes. By comparing our results for the weakly first-order SU(5) bulk phase transition against those for the stronger transitions with $N \geq 6$, we demonstrate the advantages of the LLR method for analyses of strong transitions with large latent heat.

Published
2023

72. Pipelines and Beyond: Graph Types for ADTs with Futures

Author

Rinaldi, Francis, wunder, june, De Amorim, Arthur Aevedo, and Muller, Stefan K.

Subjects
Computer Science - Programming Languages
Abstract

Parallel programs are frequently modeled as dependency or cost graphs, which can be used to detect various bugs, or simply to visualize the parallel structure of the code. However, such graphs reflect just one particular execution and are typically constructed in a post-hoc manner. Graph types, which were introduced recently to mitigate this problem, can be assigned statically to a program by a type system and compactly represent the family of all graphs that could result from the program. Unfortunately, prior work is restricted in its treatment of futures, an increasingly common and especially dynamic form of parallelism. In short, each instance of a future must be statically paired with a vertex name. Previously, this led to the restriction that futures could not be placed in collections or be used to construct data structures. Doing so is not a niche exercise: such structures form the basis of numerous algorithms that use forms of pipelining to achieve performance not attainable without futures. All but the most limited of these examples are out of reach of prior graph type systems. In this paper, we propose a graph type system that allows for almost arbitrary combinations of futures and recursive data types. We do so by indexing datatypes with a type-level vertex structure, a codata structure that supplies unique vertex names to the futures in a data structure. We prove the soundness of the system in a parallel core calculus annotated with vertex structures and associated operations. Although the calculus is annotated, this is merely for convenience in defining the type system. We prove that it is possible to annotate arbitrary recursive types with vertex structures, and show using a prototype inference engine that these annotations can be inferred from OCaml-like source code for several complex parallel algorithms., Comment: 65 pages, 41 figures, submitted to POPL 2024

Published
2023

73. On the stochastic Sine-Gordon model: an interacting field theory approach

Author

Bonicelli, Alberto, Dappiaggi, Claudio, and Rinaldi, Paolo

Subjects
Mathematical Physics, High Energy Physics - Theory, Mathematics - Probability
Abstract

We investigate the massive Sine-Gordon model in the finite ultraviolet regime on the two-dimensional Minkowski spacetime $(\mathbb{R}^2,\eta)$ with an additive Gaussian white noise. In particular we construct the expectation value and the correlation functions of a solution of the underlying stochastic partial differential equation (SPDE) as a power series in the coupling constant, proving ultimately uniform convergence. This result is obtained combining an approach first devised in [11] to study SPDEs at a perturbative level with the one discussed in [4] to construct the quantum sine-Gordon model using techniques proper of the perturbative, algebraic approach to quantum field theory (pAQFT). At a formal level the relevant expectation values are realized as the evaluation of suitably constructed functionals over $C^\infty(\mathbb{R}^2)$. In turn, these are elements of a distinguished algebra whose product is a deformation of the pointwise one, by means of a kernel which is a linear combination of two components. The first encompasses the information of the Feynmann propagator built out of an underlying Hadamard, quantum state, while the second encodes the correlation codified by the Gaussian white noise. In our analysis, first of all we extend the results obtained in [3,4] proving the existence of a convergent modified version of the S-matrix and of an interacting field as elements of the underlying algebra of functionals. Subsequently we show that it is possible to remove the contribution due to the Feynmann propagator by taking a suitable $\hbar\to 0^+$-limit, hence obtaining the sought expectation value of the solution and of the correlation functions of the SPDE associated to the stochastic Sine-Gordon model., Comment: 48 pages

Published
2023

74. GW190521: a binary black hole merger inside an active galactic nucleus?

Author

Morton, Sophia, Rinaldi, Stefano, Torres-Orjuela, Alejandro, Derdzinski, Andrea, Vaccaro, Maria Paola, and Del Pozzo, Walter

Subjects
General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

GW190521, the most massive binary black hole merger confidently detected by the LIGO-Virgo-KAGRA collaboration, is the first gravitational-wave observation of an intermediate-mass black hole. The signal was followed approximately 34 days later by flare ZTF19abanrhr, detected in AGN J124942.3+344929 by the Zwicky Transient Facility at the 78% spatial contour for GW190521s sky localization. Using the GWTC-2.1 data release, we find that the association between GW190521 and flare ZTF19abanrhr as its electromagnetic counterpart is preferred over a random coincidence of the two transients with a log Bayes factor of 8.6, corresponding to an odds ratio of $\sim$ 5400 to 1 for equal prior odds and $\sim$ 400 to 1 assuming an astrophysical prior odds of 1/13. Given the association, the multi-messenger signal allows for an estimation of the Hubble constant, finding $H_0 = 102^{+27}_{-25}\mathrm{\ km \ s^{-1} \ Mpc^{-1}}$ when solely analyzing GW190521 and $79.2^{+17.6}_{-9.6}\mathrm{\ km \ s^{-1} \ Mpc^{-1}}$ assuming prior information from the binary neutron star merger GW170817, both consistent with the existing literature., Comment: 11 pages, 5 figures

Published
2023

75. Automatic Music Playlist Generation via Simulation-based Reinforcement Learning

Author

Tomasi, Federico, Cauteruccio, Joseph, Kanoria, Surya, Ciosek, Kamil, Rinaldi, Matteo, and Dai, Zhenwen

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

Personalization of playlists is a common feature in music streaming services, but conventional techniques, such as collaborative filtering, rely on explicit assumptions regarding content quality to learn how to make recommendations. Such assumptions often result in misalignment between offline model objectives and online user satisfaction metrics. In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment. Using this simulator we develop and train a modified Deep Q-Network, the action head DQN (AH-DQN), in a manner that addresses the challenges imposed by the large state and action space of our RL formulation. The resulting policy is capable of making recommendations from large and dynamic sets of candidate items with the expectation of maximizing consumption metrics. We analyze and evaluate agents offline via simulations that use environment models trained on both public and proprietary streaming datasets. We show how these agents lead to better user-satisfaction metrics compared to baseline methods during online A/B tests. Finally, we demonstrate that performance assessments produced from our simulator are strongly correlated with observed online metric results., Comment: 10 pages. KDD 23

Published
2023
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76. A fast test for the identification and confirmation of massive black hole binaries

Author

Dotti, Massimo, Rigamonti, Fabio, Rinaldi, Stefano, Del Pozzo, Walter, Decarli, Roberto, and Buscicchio, Riccardo

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present a new observational test to identify massive black hole binaries in large multi-epoch spectroscopical catalogues and to probe the real nature of already proposed binary candidates. The test is tailored for binaries with separations large enough to allow each black hole to retain its own broad line region. In this limit the fast AGN variability typically observed over months cannot be associated to the much longer binary period, and it is assumed (as for the case of single black holes) to be the consequence of the evolution of the innermost regions of the two accretion discs. A simple analysis of the cross-correlation between different parts of individual broad emission lines can therefore identify the presence of two massive black holes whose continua vary independently of each other. Our analysis indicates that to be less affected by the noise in the spectra the broad lines should be divided in two close-to-equal-flux parts. This ensures that in the single massive black hole scenario the cross-correlation will always be high. With monitoring campaigns similar to those performed for reverberation mapping studies, on the other way, a binary can show any value of the cross-correlation and can therefore be distinguished from a standard AGN. The new test can be performed over timescales orders of magnitude shorter than the alternative tests already discussed in literature, and can be a powerful complement to the massive black hole binary search strategies already in place., Comment: 9 pages, 6 figures, accepted for publication on Astronomy & Astrophysics

Published
2023
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77. Evidence for the evolution of black hole mass function with redshift

Author

Rinaldi, Stefano, Del Pozzo, Walter, Mapelli, Michela, Lorenzo-Medina, Ana, and Dent, Thomas

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies, General Relativity and Quantum Cosmology
Abstract

We investigate the joint primary mass, mass ratio, and redshift observed distribution of astrophysical black holes using the gravitational wave events detected by the LIGO-Virgo-KAGRA collaboration and included in the third gravitational wave transient catalogue. We reconstruct this distribution using Bayesian non-parametric methods, which are data-driven models able to infer arbitrary probability densities under minimal mathematical assumptions. We find evidence for the evolution with redshift of both the primary mass and mass ratio distribution: our analysis shows the presence of two distinct sub-populations in the primary mass - redshift plane, with the lighter population, $\lesssim$ 20 $M_\odot$, disappearing at higher redshifts, $z > 0.4$. The mass ratio distribution shows no support for symmetric binaries. The observed population of coalescing binary black holes evolves with look-back time, suggesting a trend in metallicity with redshift and/or the presence of multiple, redshift-dependent formation channels., Comment: 15 pages, 13 figures

Published
2023
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78. Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks

Author

Rinaldi, Enrico, Lastre, Manuel González, Herreros, Sergio García, Ahmed, Shahnawaz, Khanahmadi, Maryam, Nori, Franco, and Muñoz, Carlos Sánchez

Subjects
Quantum Physics, Physics - Data Analysis, Statistics and Probability
Abstract

We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps. We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settings., Comment: 15 pages, 8 figures, code is available at http://github.com/CarlosSMWolff/ParamEst-NN

Published
2023

79. The deconfinement phase transition in $Sp(2N)$ gauge theories and the density of states method

Author

Mason, David, Lucini, Biagio, Piai, Maurizio, Rinaldi, Enrico, and Vadacchino, Davide

Subjects
High Energy Physics - Lattice, Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Phenomenology, High Energy Physics - Theory
Abstract

First-order phase transitions in the early universe might produce a detectable background of gravitational waves. As these phase transitions can be generated by new physics, it is important to quantify these effects. Many pure Yang-Mills gauge theories are known to undergo first-order deconfinement phase transitions, with properties that can be studied with lattice simulations. Despite the recent surge of interest in $Sp(2N)$ gauge theories as a candidate for models of physics beyond the standard model, studies of these theories at finite temperature are still very limited. In this contribution we will present preliminary results of an ongoing numerical investigation of the thermodynamic properties of the deconfinement phase transition in $Sp(4)$ Yang-Mills theory, using the linear logarithmic relaxation algorithm. This method enables us to obtain a highly accurate determination of the density of states, allowing for a precise reconstruction of thermodynamic observables. In particular, it gives access to otherwise difficult to determine quantities such as the free energy of the system, even along metastable and unstable branches, hence providing an additional direct observable to study the dynamics of the phase transition., Comment: 9 pages, 2 figures, contribution for the 40th International Symposium on Lattice Field Theory (Lattice 2023), July 31st - August 4th, 2023, Fermi National Accelerator Laboratory

Published
2023

80. More than a Monolith: Three Essays on the Diverse Journey of Immigrant Students and Their Postsecondary Outcomes

Author

Rian Rinaldi Djita

Abstract

For many years, the United States (U.S.) has been the country with the largest number of immigrants. As a result, policy about immigrant students' success becomes crucial since it carries not only ethical implications but also economic and societal implications. While there have been other alternatives for students' success, such as Career and Technical Education (CTE), undeniably, college is still seen as a common pathway to success (e.g., Choi, 2015; Heckman et al., 2018; Turner, 2004). Unfortunately, there are several gaps in the literature on immigrant students' success and postsecondary outcomes. First, while there is an increasing number of immigrants residing in other southern states, including Arkansas (Bankston III, 2007), the majority of current literature about this topic comes from more populous states such as California, Texas, Florida, and New York (Aguilar, 2010; Flores et al., 2012). Since policies about immigrant students vary from one state to another, evidence from other states is needed for a more comprehensive understanding of immigrant students' success. Second, past studies tended to aggregate immigrant students into a monolithic group, discounting their diverse postsecondary journeys. This dissertation seeks to examine the nuanced postsecondary outcomes of immigrant students across race and ethnicity groups, urbanicity, or socioeconomic status both at the national and state levels. In Chapter 2, I use nationally representative data of the High School Longitudinal Study of 2009 (HSLS:09) from the National Center for Education Statistics (NCES) to examine the relationship between parental aspirations and adolescents' educational expectations and how this may have translated into students' postsecondary outcomes among Asian populations in the U.S. Contrary to the typical portrayal of Asians as a monolithic group, I find that there are not only significant differences in parental characteristics and aspirations across Asian subgroups but also different postsecondary enrollment rates among Asian adolescents. In Chapter 3, I use administrative data from the Arkansas Department of Education (ADE) from 2007-08 to the 2018-19 academic years and the National Student Clearinghouse (NCS) of over 180,000 students who graduated from Arkansas public schools to examine Arkansas' English Language Learners (ELLs) postsecondary outcomes. I find that, aligned with evidence from other states, ELLs in Arkansas are less likely to attend postsecondary institutions compared to their non-ELL counterparts, and the trend varies not only by urbanicity or socioeconomic status but across racial and ethnic groups. Since college-level courses such as Advanced Placement (AP) and Concurrent Enrollment (CE) courses are strong predictors of students' college enrollment and long-term career success (Conger et al., 2023; Ebrahiminejad et al., 2021; Jackson, 2010), in Chapter 4, using the same data source, I examine the relationship between enrollment in AP and CE courses and student postsecondary outcomes. Despite the universal access policy in Arkansas that requires public school districts to provide students free access to these rigorous courses, enrollment in AP or CE courses still varies across students' demographic backgrounds. Specifically, I find that low-income students, ELLs, and students from other minoritized backgrounds are less likely to enroll in these rigorous courses. I also find different enrollment rates for AP and CE courses across ELLs' racial and ethnic groups. In Chapter 5, I synthesize my empirical findings and conclude that immigrant students' experiences and postsecondary education journeys in the U.S. are diverse as immigrant students face various postsecondary enrollment challenges. Therefore, policies that center around these wide-ranging populations should move beyond the broad categorization of immigrant students and be tailored more to immigrant students' specific needs by considering immigrant students' racial and ethnic backgrounds, immigration history, family characteristics, and access to resources. Understanding these complexities might help policymakers to improve college access and academic success among immigrant students. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]

Published
2024

81. Learning How to Speak Truth to Power -- Comparing Ukrainian and Swiss Foreign Language Curricula

Author

Stefanie Rinaldi and Olena Marina

Abstract

Human Rights Education strives to empower learners to participate meaningfully in a democratic and sustainable society in which human rights are guaranteed for all. Foreign language education enables students to transcend borders, gives them an opportunity to share their views, ideas, and beliefs, and contributes to the development of critical thinking skills. It can thus endow students with a 'voice' to claim and defend their rights and learn to 'speak truth to power'. This article explores "if" and "how" the intended foreign language curricula for lower secondary schools in Switzerland and Ukraine integrate human rights education. Drawing on the 2011 UN Declaration on Human Rights Education and Training as an analytical framework, the analysis reveals that 'education for human rights' is the best represented dimension in both contexts. Our results may serve as a springboard for exploring further opportunities to integrate human rights education and foreign language education.

Published
2024
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82. Applying Theory and Research toward Reducing Suicidality among Graduate Students and Postdoctoral Scholars

Author

Elizabeth A. Jach and Anthony P. Rinaldi

Abstract

Purpose: The purpose of this paper is to highlight suicide risk factors experienced by graduate students and postdoctoral scholars, and then outline suicide prevention strategies for these populations. Design/methodology/approach: Through analysis of literature and application of theory, the authors use the diathesis-stress model and Joiner's (2005) interpersonal theory of suicidality to outline suicide prevention strategies specific to graduate students and postdoctoral scholars. Findings: The authors' review of the literature and application of theory suggest that both individuals and groups can engage in suicide prevention strategies, specifically pertaining to reducing stressors unique to graduate students and postdoctoral scholars, as well as addressing feelings of thwarted belongingness and perceived burdensomeness that can lead to the development of suicidality within these populations. Practical implications: Engaging in suicide prevention strategies can save lives and address the mental health conditions exhibited among graduate student and postdoctoral scholars. Originality/value: The authors offer a synthesis of good practices addressing suicide risk factors and prevention with attention to the stress-diathesis model and Joiner's (2005) interpersonal theory of suicidality toward reducing suicidality among graduate students and postdoctoral scholars.

Published
2024
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83. Neurobiological modulation with REAC technology: enhancing pain, depression, anxiety, stress, and quality of life in post-polio syndrome subjects

Author

Jeyce Adrielly André Nogueira, Acary Souza Bulle Oliveira, Monalisa Pereira Motta, Alcione Aparecida Vieira de Souza Moscardi, Vanessa Manchim Favaro, Claudete Munhoz Teixeira, Amanda Orasmo Simcsik, Maria Clara Patrizi, Maria Salete Conde, Arianna Rinaldi, Vania Fontani, and Salvatore Rinaldi

Subjects
Post-polio syndrome, REAC technology, Pain, Depression, Anxiety, Stress, Medicine, Science
Abstract

Abstract Post-polio syndrome (PPS) brings new challenges for polio survivors, including muscle decline, pain, depression, and diminished quality of life. This study explored the potential of REAC neuromodulatory treatments to ease pain, improve mood, and enhance quality of life in PPS patients. 17 individuals with PPS (average age 54.8) received three REAC treatments: Neuro Postural Optimization, Neuro Psycho Physical Optimization, and Neuro Psycho Physical Optimization-Cervico Brachial. Pain, depression, anxiety, stress, and quality of life were assessed before and after using established scales. REAC treatments significantly reduced pain across various dimensions, along with depression, anxiety, and stress levels. Additionally, patients reported improved physical and psychological quality of life. This study suggests REAC neuromodulatory treatments as a promising non-invasive option to improve pain, emotional well-being, and quality of life in individuals with PPS.

Published
2024
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89. Expert consensus on the diagnosis and treatment of end-stage liver disease complicated by infections

Author

Chen, Tao, Chen, Guang, Wang, Guiqiang, Treeprasertsuk, Sombat, Lesmana, Cosmas Rinaldi Adithya, Lin, Han-Chieh, Al-mahtab, Mamun, Chawla, Yogesh K., Tan, Soek-Siam, Kao, Jia-Horng, Yuen, Man-Fung, Lee, Guan-Huei, Alcantara-Payawal, Diana, Nakayama, Nobuaki, Abbas, Zaigham, Jafri, Wasim, Kim, Dong-Joon, Choudhury, Ashok, Mahiwall, Rakhi, Hou, Jinlin, Hamid, Saeed, Jia, Jidong, Bajaj, J. S., Wang, Fusheng, Sarin, Shiv K., and Ning, Qin

Published
2024
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