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8,485 results on '"Rosca, A."'

1. Neural Compression of Atmospheric States

Author

Mirowski, Piotr, Warde-Farley, David, Rosca, Mihaela, Grimes, Matthew Koichi, Hasson, Yana, Kim, Hyunjik, Rey, Mélanie, Osindero, Simon, Ravuri, Suman, and Mohamed, Shakir

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Physics - Atmospheric and Oceanic Physics
Abstract

Atmospheric states derived from reanalysis comprise a substantial portion of weather and climate simulation outputs. Many stakeholders -- such as researchers, policy makers, and insurers -- use this data to better understand the earth system and guide policy decisions. Atmospheric states have also received increased interest as machine learning approaches to weather prediction have shown promising results. A key issue for all audiences is that dense time series of these high-dimensional states comprise an enormous amount of data, precluding all but the most well resourced groups from accessing and using historical data and future projections. To address this problem, we propose a method for compressing atmospheric states using methods from the neural network literature, adapting spherical data to processing by conventional neural architectures through the use of the area-preserving HEALPix projection. We investigate two model classes for building neural compressors: the hyperprior model from the neural image compression literature and recent vector-quantised models. We show that both families of models satisfy the desiderata of small average error, a small number of high-error reconstructed pixels, faithful reproduction of extreme events such as hurricanes and heatwaves, preservation of the spectral power distribution across spatial scales. We demonstrate compression ratios in excess of 1000x, with compression and decompression at a rate of approximately one second per global atmospheric state., Comment: 44 pages, 25 figures

Published
2024

2. 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

3. Corridor Geometry in Gradient-Based Optimization

Author

Dherin, Benoit and Rosca, Mihaela

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract

We characterize regions of a loss surface as corridors when the continuous curves of steepest descent -- the solutions of the gradient flow -- become straight lines. We show that corridors provide insights into gradient-based optimization, since corridors are exactly the regions where gradient descent and the gradient flow follow the same trajectory, while the loss decreases linearly. As a result, inside corridors there are no implicit regularization effects or training instabilities that have been shown to occur due to the drift between gradient descent and the gradient flow. Using the loss linear decrease on corridors, we devise a learning rate adaptation scheme for gradient descent; we call this scheme Corridor Learning Rate (CLR). The CLR formulation coincides with a special case of Polyak step-size, discovered in the context of convex optimization. The Polyak step-size has been shown recently to have also good convergence properties for neural networks; we further confirm this here with results on CIFAR-10 and ImageNet.

Published
2024

8. Impact of smoking in patients with suspected coronary artery disease in the randomised DISCHARGE trial

Author

Mancone, Massimo, Mézquita, Aldo J. Vázquez, Birtolo, Lucia Ilaria, Maurovich-Horvat, Pal, Kofoed, Klaus F., Benedek, Theodora, Donnelly, Patrick, Rodriguez-Palomares, Jose, Erglis, Andrejs, Štěchovský, Cyril, Šakalytė, Gintare, Ađić, Nada Čemerlić, Gutberlet, Matthias, Diez, Ignacio, Davis, Gershan, Zimmermann, Elke, Kępka, Cezary, Vidakovic, Radosav, Francone, Marco, Ilnicka-Suckiel, Małgorzata, Plank, Fabian, Knuuti, Juhanni, Faria, Rita, Schröder, Stephen, Berry, Colin, Saba, Luca, Ruzsics, Balazs, Rieckmann, Nina, Kubiak, Christine, Hansen, Kristian Schultz, Müller-Nordhorn, Jaqueline, Merkely, Bela, Sigvardsen, Per E., Benedek, Imre, Orr, Clare, Valente, Filipa Xavier, Zvaigzne, Ligita, Suchánek, Vojtěch, Jankauskas, Antanas, Ađić, Filip, Woinke, Michael, Keane, Stephen, Lecumberri, Ignacio, Thwaite, Erica, Kruk, Mariusz, Jovanovic, Vladimir, Kuśmierz, Donata, Feuchtner, Gudren, Pietilä, Mikko, Ribeiro, Vasco Gama, Drosch, Tanja, Delles, Christian, Palmisano, Vitanio, Fisher, Michael, Drobni, Zsófia D., Kragelund, Charlotte, Aurelian, Rosca, Kelly, Stephanie, del Blanco, Bruno Garcia, Rubio, Ainhoa, Boussoussou, Melinda, Hove, Jens D., Rodean, Ioana, Regan, Susan, Calabria, Hug Cuéllar, Becker, Dávid, Larsen, Linnea, Hodas, Roxana, Napp, Adriane E., Haase, Robert, Feger, Sarah, Mohamed, Mahmoud, Neumann, Konrad, Dreger, Henryk, Rief, Matthias, Wieske, Viktoria, Douglas, Pamela S., Estrella, Melanie, Bosserdt, Maria, Martus, Peter, Serna-Higuita, Lina M., Dodd, Jonathan D., and Dewey, Marc

Published
2024
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10. Boson star head-on collisions with constraint-violating and constraint-satisfying initial data

Author

Atteneder, Florian, Rüter, Hannes R., Cors, Daniela, Rosca-Mead, Roxana, Hilditch, David, and Brügmann, Bernd

Subjects
General Relativity and Quantum Cosmology
Abstract

Simulations of binary collisions involving compact objects require initial data that satisfy the constraint equations of general relativity. For binary boson star simulations it is common practice to use a superposition of two isolated star solutions to construct an approximate solution to the constraint equations. Such superposed data is simple to set up compared to solving these equations explicitly, but also introduces extra constraint violations in the time evolution. In this work we investigate how physical observables depend on the quality of initial data in the case of head-on boson star collisions. In particular we compare results obtained from data prepared using four different methods: the standard method to superpose isolated stars, a heuristic improvement to this superposition technique and two versions of this data where excess constraint violations were removed through a conformal thin-sandwich solver. We find that differences in the time evolutions are dominated by differences in the way the two superposition methods differ, whereas additionally constraint solving the superposed data has smaller impact. The numerical experiments are conducted using the pseudo-spectral code bamps. Our work demonstrates that bamps is a code suited for generating high accuracy numerical waveforms for boson star collisions due to the exponential convergence in the polynomial resolution of the numerical approximation.

Published
2023
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11. On discretisation drift and smoothness regularisation in neural network training

Author

Rosca, Mihaela Claudia

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

The deep learning recipe of casting real-world problems as mathematical optimisation and tackling the optimisation by training deep neural networks using gradient-based optimisation has undoubtedly proven to be a fruitful one. The understanding behind why deep learning works, however, has lagged behind its practical significance. We aim to make steps towards an improved understanding of deep learning with a focus on optimisation and model regularisation. We start by investigating gradient descent (GD), a discrete-time algorithm at the basis of most popular deep learning optimisation algorithms. Understanding the dynamics of GD has been hindered by the presence of discretisation drift, the numerical integration error between GD and its often studied continuous-time counterpart, the negative gradient flow (NGF). To add to the toolkit available to study GD, we derive novel continuous-time flows that account for discretisation drift. Unlike the NGF, these new flows can be used to describe learning rate specific behaviours of GD, such as training instabilities observed in supervised learning and two-player games. We then translate insights from continuous time into mitigation strategies for unstable GD dynamics, by constructing novel learning rate schedules and regularisers that do not require additional hyperparameters. Like optimisation, smoothness regularisation is another pillar of deep learning's success with wide use in supervised learning and generative modelling. Despite their individual significance, the interactions between smoothness regularisation and optimisation have yet to be explored. We find that smoothness regularisation affects optimisation across multiple deep learning domains, and that incorporating smoothness regularisation in reinforcement learning leads to a performance boost that can be recovered using adaptions to optimisation methods., Comment: PhD thesis. arXiv admin note: text overlap with arXiv:2302.01952

Published
2023

12. Boson stars in massless and massive scalar-tensor gravity

Author

Evstafyeva, Tamara, Rosca-Mead, Roxana, Sperhake, Ulrich, and Brugmann, Bernd

Subjects
General Relativity and Quantum Cosmology
Abstract

We study phenomenological features and stability of boson stars in massless and massive scalar-tensor theory of gravity with Damour-Esposito-Farese coupling. This coupling between the tensor and scalar sectors of the theory leads to a phenomenon called spontaneous scalarization, the onset of which we investigate by numerically computing families of boson-star models using shooting and relaxation algorithms. We systematically explore the effects of the theory's coupling, the mass of the gravitational scalar and the choice of the bosonic potential on the structure of weakly and strongly scalarized solutions. Scalarized boson-star models share many common features with neutron stars in the same scalar-tensor theory of gravity. In particular, scalarization can result in boson stars with significantly larger radii and masses, which tend to be energetically favored over their weakly or non-scalarized counterparts. Overall, we find that boson stars are not quite as susceptible to scalarization as neutron stars., Comment: 26 pages, 20 figures, to match version published in PRD (due to the large number of figures, some panels may appear blank if viewed from the browser, we recommend downloading the paper and viewing it locally to avoid these issues)

Published
2023
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13. Robotic Defect Inspection with Visual and Tactile Perception for Large-scale Components

Author

Agarwal, Arpit, Ajith, Abhiroop, Wen, Chengtao, Stryzheus, Veniamin, Miller, Brian, Chen, Matthew, Johnson, Micah K., Rincon, Jose Luis Susa, Rosca, Justinian, and Yuan, Wenzhen

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

In manufacturing processes, surface inspection is a key requirement for quality assessment and damage localization. Due to this, automated surface anomaly detection has become a promising area of research in various industrial inspection systems. A particular challenge in industries with large-scale components, like aircraft and heavy machinery, is inspecting large parts with very small defect dimensions. Moreover, these parts can be of curved shapes. To address this challenge, we present a 2-stage multi-modal inspection pipeline with visual and tactile sensing. Our approach combines the best of both visual and tactile sensing by identifying and localizing defects using a global view (vision) and using the localized area for tactile scanning for identifying remaining defects. To benchmark our approach, we propose a novel real-world dataset with multiple metallic defect types per image, collected in the production environments on real aerospace manufacturing parts, as well as online robot experiments in two environments. Our approach is able to identify 85% defects using Stage I and identify 100% defects after Stage II. The dataset is publicly available at https://zenodo.org/record/8327713, Comment: This is a pre-print for International Conference on Intelligent Robots and Systems 2023 publication

Published
2023

14. Prescription opioid use in Israel – the tide has risen, but it’s not a tidal wave

Author

Yehuda Neumark and Paola Rosca

Subjects
Opioid prescription use, Opioids, OUD, Israel, Policy, Fentanyl, Medicine (General), R5-920, Public aspects of medicine, RA1-1270
Abstract

Abstract The devastating human and financial costs of the ongoing global opioid crisis underscore the need for comprehensive public health strategies, effective treatment programs, and robust policy interventions to mitigate its impact. Regarding Israel, numerous reports highlight a steady increase since 2000 in prescription opioid use and the shift to more potent opioids particularly fentanyl, particularly among more marginalized population groups. In response to growing concern in the country about the rise in prescription opioid use and the consequential risk of opioid use disorder, the Israeli government, together with the country’s health service providers, implemented a series of measures to monitor and regulate opioid prescriptions and balance the need for effective pain management with the prevention of opioid abuse and its associated harms. A national opioid data monitoring system is being established, alongside the provision of addiction training for health professionals, the integration of treatment services for opioid use disorder into the nationalized primary healthcare system, and the expansion of harm reduction strategies to mitigate the health risks associated with opioid use. Additional funding for opioid-related research, and for the broader fields of addictions and mental health, is vital. In conclusion, the sum of the evidence suggests that Israel is not facing an “opioid crisis” Continued commitment, resources, and innovative approaches will be crucial to ensure that the rising tide of opioid use in Israel, particularly during and in the aftermath of the ongoing war, will not become a tidal wave.

Published
2024
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15. Utilization of Green Mussel (Perna viridis) as A Lead Metal Pollutant Adorbent in Batik Dye Waste

Author

Savalda Rosca Pradipta, Juni Triastuti, and Nina Nurmalia Dewi

Subjects
adsorbent, green mussel shell, surface area, lead, Naval Science
Abstract

Lead (Pb) from batik dyeing waste that is thrown into the environment can pollute the environment so it needs processing before being thrown into the environment. The aim of this research is to determine the effect of green mussel shells as an adsorbent with different concentrations on reducing Pb heavy metal contamination in batik dyeing waste and to determine the optimal concentration of green mussel shells to reduce Pb heavy metal contamination in batik dyeing waste. The research method used was experimental with 4 treatments with different concentrations of green mussel shell ash as an adsorbent (0, 10, 20, and 30%). The results of the research showed that a green mussel shell ash concentration of 30% was the best concentration because it was able to reduce Pb levels in batik dyeing waste by 68.52%. Characterization of green mussel shell ash includes average pore radius (10.674 nm), total pore volume (0.005 cc/g), and surface area (0.969 m2/g).

Published
2024
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17. Implicit regularisation in stochastic gradient descent: from single-objective to two-player games

Author

Rosca, Mihaela and Deisenroth, Marc Peter

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

Recent years have seen many insights on deep learning optimisation being brought forward by finding implicit regularisation effects of commonly used gradient-based optimisers. Understanding implicit regularisation can not only shed light on optimisation dynamics, but it can also be used to improve performance and stability across problem domains, from supervised learning to two-player games such as Generative Adversarial Networks. An avenue for finding such implicit regularisation effects has been quantifying the discretisation errors of discrete optimisers via continuous-time flows constructed by backward error analysis (BEA). The current usage of BEA is not without limitations, since not all the vector fields of continuous-time flows obtained using BEA can be written as a gradient, hindering the construction of modified losses revealing implicit regularisers. In this work, we provide a novel approach to use BEA, and show how our approach can be used to construct continuous-time flows with vector fields that can be written as gradients. We then use this to find previously unknown implicit regularisation effects, such as those induced by multiple stochastic gradient descent steps while accounting for the exact data batches used in the updates, and in generally differentiable two-player games.

Published
2023

18. Investigating the Edge of Stability Phenomenon in Reinforcement Learning

Author

Iordan, Rares, Deisenroth, Marc Peter, and Rosca, Mihaela

Subjects
Computer Science - Machine Learning
Abstract

Recent progress has been made in understanding optimisation dynamics in neural networks trained with full-batch gradient descent with momentum with the uncovering of the edge of stability phenomenon in supervised learning. The edge of stability phenomenon occurs as the leading eigenvalue of the Hessian reaches the divergence threshold of the underlying optimisation algorithm for a quadratic loss, after which it starts oscillating around the threshold, and the loss starts to exhibit local instability but decreases over long time frames. In this work, we explore the edge of stability phenomenon in reinforcement learning (RL), specifically off-policy Q-learning algorithms across a variety of data regimes, from offline to online RL. Our experiments reveal that, despite significant differences to supervised learning, such as non-stationarity of the data distribution and the use of bootstrapping, the edge of stability phenomenon can be present in off-policy deep RL. Unlike supervised learning, however, we observe strong differences depending on the underlying loss, with DQN -- using a Huber loss -- showing a strong edge of stability effect that we do not observe with C51 -- using a cross entropy loss. Our results suggest that, while neural network structure can lead to optimisation dynamics that transfer between problem domains, certain aspects of deep RL optimisation can differentiate it from domains such as supervised learning.

Published
2023

21. Effects of prosthesis-patient mismatch on cardiac function and clinical outcome after transcatheter aortic valve implantation

Author

Parasca Catalina A., Calin Andreea, Rosca Monica, Botezatu Simona, Enache Roxana, Beladan Carmen, Deleanu Dan, Chioncel Ovidiu, Bubenek-Turconi Serban, Iliescu Vlad A., and Popescu Bogdan A.

Subjects
aortic stenosis, tavi, prosthesis-patient mismatch, la function, prognosis, Internal medicine, RC31-1245
Abstract

Transcatheter aortic valve implantation (TAVI) prostheses have better hemodynamics compared to surgical prostheses, with a lower incidence of prosthesis-patient mismatch (PPM). Nonetheless, the effects of PPM have been controversial regarding clinical impact. This study aims to determine the short-term impact of PPM on cardiac function and the effect of PPM on mid-term outcome in patients undergoing transfemoral TAVI with balloon-expandable valves.

Published
2024
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22. Dupilumab in Patients with Atopic Dermatitis: A Multicentric, Long-Term, Real-World Portuguese Study

Author

Tiago Torres, Maria João Cruz, Margarida Gonçalo, Paulo Filipe, Bruno Duarte, João Alves, José Miguel Alvarenga, Gilberto Rosa, Duarte Flor, José Ramos, Diogo Sousa, Aureliu Rosca, César Magalhães, Cristina Claro, Joana Rocha, Catarina Vilarinho, Fernando Mota, Alberto Mota, and Maria João Paiva Lopes

Subjects
Atopic dermatitis, Dupilumab, Drug Survival, Long-term, Effectiveness, Dermatology, RL1-803
Abstract

Abstract Introduction Several clinical trials have established the efficacy and safety of dupilumab for treating atopic dermatitis (AD). However, literature remains scarce in reporting the long-term effectiveness, safety, and drug survival of dupilumab in real-world settings. This study aimed to describe the latter outcomes of dupilumab in patients with AD. Methods This Portuguese, multicentric, observational, retrospective study included consecutive adult patients with AD who initiated dupilumab between January 2019 and September 2023, with a follow-up period up to 30 months. Drug discontinuation and adverse effects data were used to estimate drug survival. Clinical assessments included the Eczema Area and Severity Index (EASI), pruritus numerical rating scale (NRS), and Dermatology Life Quality Index (DLQI). Results A total of 312 patients were included in the study, with 56.4% being male (median age of 30 years, range 18–83). The 30-month drug survival rate was 82.0%. During the study period, 12.5% of the sample (n = 39 patients) discontinued treatment: 7.3% due to treatment failure, 2.9% due to safety concerns, 1.3% due to complete disease control, 0.6% due to pregnancy, and 0.3% due to lack of compliance. Adverse events not leading to drug discontinuation were noted in 25.6% of the sample (n = 80). Conjunctivitis was the most frequently reported adverse event (17%), followed by facial erythema (9%). At 30 months, the mean EASI decreased significantly from 27.30 ± 11.89 at baseline to 2.92 ± 3.96 (p

Published
2024
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23. ARTIFICIAL INTELLIGENCE – POWERED VIDEO CONTENT GENERATION TOOLS

Author

Cosmina – Mihaela Rosca, Ionut Adrian Gortoescu, and Marius Radu Tănase

Subjects
ai-powered video content generation, ethical considerations, deep fake technology, responsible ai principles, evaluation criteria, Technology (General), T1-995, Science
Abstract

This article discusses the considerations of artificial intelligence-powered video content generation tools, exploring their applications, ethical considerations, and evaluation criteria. Through discussions of various artificial intelligence (AI) tools, including features, limitations, and implications, the authors analyze the evolving landscape of video creation in the digital age. Key themes include the ethical implications of deep fake technology and the importance of responsible AI principles, exemplified by Microsoft's guidelines. This paper identifies five of the most promoted free social media tools. Evaluation criteria for these tools, such as visual quality, relevance, coherence, authenticity, and transparency, are examined to assess the suitability of AI-generated videos. While AI offers promising opportunities, the discussion underscores the continued need for human oversight and ethical considerations to ensure the responsible use of AI technologies in video content generation.

Published
2024
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25. Stochastic gravitational wave background from supernovae in massive scalar-tensor gravity

Author

Rosca-Mead, Roxana, Agathos, Michalis, Moore, Christopher J., and Sperhake, Ulrich

Subjects
General Relativity and Quantum Cosmology
Abstract

In massive scalar-tensor gravity, core-collapse supernovae are strong sources of scalar-polarized gravitational waves. These can be detectable out to large distance. The dispersive nature of the propagation of waves in the massive scalar field mean the gravitational wave signals are long lived and many such signals can overlap to form a stochastic background. Using different models for the population of supernova events in the nearby universe, we compute predictions for the energy-density in the stochastic scalar-polarized gravitational wave background from core-collapse events in massive scalar-tensor gravity for theory parameters that facilitate strong scalarization. The resulting energy density is below the current constraints on a Gaussian stochastic gravitational wave background but large enough to be detectable with the current generation of detectors when they reach design sensitivity, indicating that it will soon be possible to place new constraints on the parameter space of massive scalar-tensor gravity., Comment: to match published version in Phys.Rev.D

Published
2023
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26. On a continuous time model of gradient descent dynamics and instability in deep learning

Author

Rosca, Mihaela, Wu, Yan, Qin, Chongli, and Dherin, Benoit

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

The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however, and particularly its instability, has lagged behind its empirical success. To add to the theoretical tools available to study gradient descent we propose the principal flow (PF), a continuous time flow that approximates gradient descent dynamics. To our knowledge, the PF is the only continuous flow that captures the divergent and oscillatory behaviors of gradient descent, including escaping local minima and saddle points. Through its dependence on the eigendecomposition of the Hessian the PF sheds light on the recently observed edge of stability phenomena in deep learning. Using our new understanding of instability we propose a learning rate adaptation method which enables us to control the trade-off between training stability and test set evaluation performance., Comment: Transactions of Machine Learning Research, 2023

Published
2023

27. Kinematic and Dynamic Analysis of a New Prototype Exoskeleton for Human Lower Limb Rehabilitation

Author

Geonea, Ionut, Racila, Laurentiu, Dumitru, Ilie, Grigorie, Laura, Romanescu, Alina, Rosca, Sorin, Tarnita, Daniela, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Romdhane, Lotfi, editor, Mlika, Abdelfattah, editor, Zeghloul, Saïd, editor, Chaker, Abdelbadia, editor, and Laribi, Med Amine, editor

Published
2024
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31. Comparing Two Different Implementations of OPC UA Clients

Author

Covrig, Tudor, Ciobotaru, Alexandru, Duka, Adrian-Vasile, Roșca, Ovidiu-Alexandru, Miclea, Liviu, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Moldovan, Liviu, editor, and Gligor, Adrian, editor

Published
2024
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32. Sound Absorption Characteristics of Orthotropic Porous Materials

Author

Guiman, Maria Violeta, Roșca, Ioan Călin, Nastac, Silviu Marian, Georgescu, Sergiu Valeriu, Câmpean, Mihaela, Stanciu, Mariana Domnica, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Moldovan, Liviu, editor, and Gligor, Adrian, editor

Published
2024
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34. Low-Cost Tester for Electrical Safety of the Medical Devices

Author

Drugă, C., Serban, Ional, Roşca, I. C., Braun, Barbu, Magjarević, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Vlad, Simona, editor, and Roman, Nicolae Marius, editor

Published
2024
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35. Measurement of Biokinetic Parameters with the CvMob Program at the Level of the Lower Limb with a Functional 3D Printed Knee Orthosis

Author

Tulică, Alexandru-Constantin, Roșca, Ileana-Constanța, Drugă, Corneliu-Nicolae, Șerban, Ionel, Magjarević, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Sontea, Victor, editor, Tiginyanu, Ion, editor, and Railean, Serghei, editor

Published
2024
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37. Why neural networks find simple solutions: the many regularizers of geometric complexity

Author

Dherin, Benoit, Munn, Michael, Rosca, Mihaela, and Barrett, David G. T.

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

In many contexts, simpler models are preferable to more complex models and the control of this model complexity is the goal for many methods in machine learning such as regularization, hyperparameter tuning and architecture design. In deep learning, it has been difficult to understand the underlying mechanisms of complexity control, since many traditional measures are not naturally suitable for deep neural networks. Here we develop the notion of geometric complexity, which is a measure of the variability of the model function, computed using a discrete Dirichlet energy. Using a combination of theoretical arguments and empirical results, we show that many common training heuristics such as parameter norm regularization, spectral norm regularization, flatness regularization, implicit gradient regularization, noise regularization and the choice of parameter initialization all act to control geometric complexity, providing a unifying framework in which to characterize the behavior of deep learning models., Comment: Accepted as a NeurIPS 2022 paper

Published
2022

38. Evaluating Hepatotoxicity: A Comparative Analysis of New Generation versus Historical Antiretroviral Agents

Author

Simona-Alina Abu-Awwad, Ahmed Abu-Awwad, Madalina-Ianca Suba, Voichita Elena Lazureanu, Andrei-Daniel Bolovan, Ovidiu Rosca, Mirela-Mădălina Turaiche, Adela-Teodora Benea, and Bogdan Hogea

Subjects
HIV, antiretroviral therapy, liver metabolic health, hepatic metabolism, liver function, latest-generation ARV therapies, Other systems of medicine, RZ201-999
Abstract

(1) Background: Since the advent of zidovudine in 1987, antiretroviral therapy has undergone significant evolution, marked by the introduction of 34 antiretroviral drugs and 24 fixed-dose combinations. Despite these advances, hepatotoxicity remains a formidable challenge, influencing morbidity, mortality, and treatment adherence in HIV-infected patients. This study aims to compare the hepatotoxic effects of latest-generation antiretroviral medications with those of older-generation therapies, assessing their long-term impact on liver health in HIV patients. (2) Methods: This retrospective study analyzed data from 304 HIV patients treated with either latest-generation or older-generation antiretroviral drugs over four years. Patients were monitored for hepatotoxicity through liver function tests at diagnosis, six months, and one-year post-treatment initiation. (3) Results: Initial and six-month liver function tests showed no significant differences between the two groups. However, at one-year post-treatment, patients on latest-generation antiretrovirals exhibited significant improvements in ALT, AST, and ALP levels, suggesting a better safety profile regarding hepatotoxicity. Additionally, a significantly lower incidence of splenomegaly was observed in patients treated with newer medications. (4) Conclusions: The findings suggest that the latest-generation antiretroviral medications may offer a safer profile in terms of hepatotoxicity compared to older therapies, with potential benefits for long-term liver health. This study underscores the importance of continuous monitoring and further research to optimize ART strategies, ensuring improved patient outcomes and quality of life for individuals living with HIV.

Published
2024
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40. Consideration on New Application for Rotary Engine

Author

Roșca, Adrian Sorin, Dumitru, Ilie, Geonea, Ionuț, Roșca, Doina, Zheng, Zheng, Editor-in-Chief, Xi, Zhiyu, Associate Editor, Gong, Siqian, Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Baochang, Series Editor, Zhang, Wei, Series Editor, Zhu, Quanxin, Series Editor, Zheng, Wei, Series Editor, Dumitru, Ilie, editor, Matei, Lucian, editor, Racila, Laurentiu Daniel, editor, and Rosca, Adrian Sorin, editor

Published
2023
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42. List of contributors

Author

A., Brunda, primary, Abolanle, Kayode, additional, Achar, Raghu Ram, additional, Adam, Gareeballah Osman, additional, Afif, Sadaf, additional, Akhtar, Mohd. Sayeed, additional, Aly, Shaza H., additional, Andrei-Bitere, Iulia, additional, Bhat, Smitha S., additional, Butorac, Mark, additional, Charles, Adetunji Oluwaseun, additional, Chu, Mathew, additional, Cox, Madeleine J., additional, Cozma, Elena Codruta, additional, Cozma, Matei Alexandru, additional, Cretoiu, Sanda Maria, additional, Dable-Tupas, Genevieve, additional, Devegowda, Devananda, additional, Diaconu, Camelia Cristina, additional, Dionisie, Vlad, additional, Dugăeşescu, Monica, additional, Duruchukwu, Esther, additional, Eldahshan, Omayma A., additional, El-Shazly, Mohamed, additional, Ephraums, James, additional, H.V., Nachith, additional, Himathi, MU, additional, Ionescu, Ruxandra Florentina, additional, Jafri, Sabeen, additional, Kumar, Maneesh, additional, Loudovikou, Anna, additional, Mandea, Matei, additional, Maurya, Rahul Kumar, additional, Messaoudi, Mahommed, additional, Miu, Andra Daniela, additional, Moisa, Emanuel, additional, Palai, Santwana, additional, Pavelescu, Luciana Alexandra, additional, Prasad, Shashanka K., additional, Profir, Monica, additional, Pszczółkowski, Piotr, additional, R., Sindhu, additional, Rosca, Ana Maria, additional, Rozenbroek, Paul, additional, Roşu, Oana Alexandra, additional, Ryu, Ji Hyun, additional, Sawicka, Barbara, additional, Soare, Cristina, additional, Stoian, Anastasia, additional, Tripathi, Alok Shiomurti, additional, Tsagkaris, Christos, additional, Tsang, Samuel, additional, Wright, Erin, additional, Xu, Mary, additional, and Yasir, Mohammad, additional

Published
2024
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43. Antidodes in esthetic medicine

Author

Soare, Cristina, primary, Rosca, Ana Maria, additional, Miu, Andra Daniela, additional, Stoian, Anastasia, additional, and Cozma, Elena Codruta, additional

Published
2024
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45. Prevalence of bacterial vaginosis among pregnant women attending antenatal care in low‐ and middle‐income countries between 2000 and 2020: A systematic review and meta‐analysis

Author

Faith Jiyeong Park, Aliona S. Rosca, Piet Cools, and R. Matthew Chico

Subjects
antenatal care, bacterial vaginosis, low‐ and middle‐income countries, pregnant women, prevalence, Reproduction, QH471-489, Women. Feminism, HQ1101-2030.7
Abstract

Abstract Background Bacterial vaginosis increases risk of preterm birth and low birthweight, adverse pregnancy outcomes that disproportionately affect low‐ and middle‐income countries (LMICs). Objectives We aimed to estimate the prevalence of bacterial vaginosis among pregnant women attending antenatal care in LMICs between 2000 and 2020. Search Strategy We conducted a systematic review of PubMed, Embase and five regional databases. Selection Criteria We included studies conducted in LMICs and published between 2000 and 2020 in which bacterial vaginosis prevalence was reported among pregnant women attending antenatal care. Data Collection and Analysis We corrected point estimates and applied random‐effects models to generate pool prevalence estimates. We carried out subgroup analyses by study year, country‐income level, HIV prevalence, sample size, diagnostic method, trimester of pregnancy, presence of symptoms at diagnosis and risk of bias. Main Results Of 1132 publications, 74 studies met inclusion criteria, contributing 80 data points from 46 661 pregnant women. Overall pooled mean prevalence across LMICs was 15.7%. Regional prevalence ranged from 25.1% in sub‐Saharan Africa to 7.4% in Central and Southern Asia. Prevalence was 33.4% in studies where HIV prevalence was ≥10%, and 6.6% in which HIV prevalence was

Published
2024
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48. Indicatori cercetare-dezvoltare 2020: Studiu privind asigurarea suportului metodologic pentru monitorizarea indicatorilor Strategiei de Cercetare-Dezvoltare a Republicii Moldova până în anul 2020

Author

Cojocaru, Igor, primary, Cuciureanu, Gheorghe, additional, Turcan, Nelly, additional, Cujba, Rodica, additional, Minciuna, Vitalie, additional, Holban, Ion, additional, Rosca, Alfreda, additional, Grecu, Mihai, additional, Cojocaru, Irina, additional, Botnaru, Viorica, additional, Lemet, Elizaveta, additional, Guzun, Mihail, additional, Ungureanu, Elena, additional, Munteanu, Ruslan, additional, Cazacu, Elena, additional, and Turcan, Andrei, additional

Published
2023
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49. Contemporary management and outcomes of peripheral venous aneurysms: A multi-institutional study

Author

Patel, Rhusheet, Woo, Karen, Wakefield, Thomas W, Beaulieu, Robert J, Khashram, Manar, De Caridi, Giovanni, Benedetto, Filippo, Shalhub, Sherene, El-Ghazali, Asmaa, Silpe, Jeffrey E, Rosca, Mihai, Cohnert, Tina U, Siegl, Gregor K, Abularrage, Christopher, Sorber, Rebecca, Wittgen, Catherine M, Bove, Paul G, Long, Graham W, Charlton-Ouw, Kristofer M, Ray, Hunter M, Lawrence, Peter, and Baril, Donald

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Neurosciences, Cardiovascular, Patient Safety, Chronic Pain, Clinical Research, Pain Research, Hematology, 6.4 Surgery, Evaluation of treatments and therapeutic interventions, Aneurysm, Anticoagulants, Fibrinolytic Agents, Humans, Lower Extremity, Pain, Popliteal Vein, Pulmonary Embolism, Retrospective Studies, Risk Factors, Venous Thromboembolism, Deep vein thrombosis, DVT, Multi-Institutional, Venous aneurysm
Abstract

ObjectiveExtremity venous aneurysms result in the risk of pulmonary embolism (PE) and chronic venous insufficiency. At present, owing to the rarity of these aneurysms, no consensus for their treatment has been established. The purpose of the present study was to review the presentation, natural history, and contemporary management of extremity venous aneurysms.MethodsWe performed a retrospective, multi-institutional review of all patients with extremity venous aneurysms treated from 2008 to 2018. A venous aneurysm was defined as saccular or fusiform with an aneurysm/vein ratio of >1.5.ResultsA total of 66 extremity aneurysms from 11 institutions were analyzed, 40 of which were in a popliteal location, 14 iliofemoral, and 12 in an upper extremity or a jugular location. The median follow-up was 27 months (range, 0-120 months). Of the 40 popliteal venous aneurysms, 8 (20%) had presented with deep vein thrombosis (DVT) or PE, 13 (33%) had presented with pain, and 19 had been discovered incidentally. The mean size of the popliteal venous aneurysms presenting with DVT or PE was larger than that of those presenting without thromboembolism (3.8 cm vs 2.5 cm; P = .003). Saccular aneurysm morphology in the lower extremity was associated with thromboembolism (30% vs 9%; P = .046) and fusiform aneurysm morphology with a thrombus burden >25% (45% vs 3%). Patients presenting with thromboembolism were more likely to have had a thrombus burden >25% in their lower extremity venous aneurysm compared with those who had presented without thromboembolism (70% vs 9%). Approximately half of all the patients underwent immediate intervention, and half were managed with observation or antithrombotic regimen. In the non-operative cohort, three patients subsequently developed a DVT. Eight patients in the medically managed cohort went on to require surgical intervention. Of the 12 upper extremity venous aneurysms, none had presented with DVT or PE, and only 2 (17%) had presented with pain. Of the 66 patients in the entire cohort, 41 underwent surgical intervention. The most common indication was the absolute aneurysm size. Nine patients had undergone surgery because of a DVT or PE, and 11 for pain or extremity swelling. The most common surgery was aneurysmorrhaphy in 21 patients (53%), followed by excision and ligation in 14 patients (35%). Five patients (12%) had undergone interposition bypass grafting. A postoperative hematoma requiring reintervention was the most common complication, occurring in three popliteal vein repairs and one iliofemoral vein repair. None of the patients, treated either surgically or medically, had reported post-thrombotic complications during the follow-up period.ConclusionsLarge lower extremity venous aneurysms and saccular aneurysms with thrombus >25% of the lumen are more likely to present with thromboembolic complications. Surgical intervention for lower extremity venous aneurysms is indicated to reduce the risk of venous thromboembolism (VTE) and the need for continued anticoagulation. Popliteal aneurysms >2.5 cm and all iliofemoral aneurysms should be considered for repair. Upper extremity aneurysms do not have a significant risk of VTE and warrant treatment primarily for symptoms other than VTE.

Published
2022

50. Megahertz‐rate ultrafast X‐ray scattering and holographic imaging at the European XFEL

Author

Hagström, Nanna Zhou, Schneider, Michael, Kerber, Nico, Yaroslavtsev, Alexander, Parra, Erick Burgos, Beg, Marijan, Lang, Martin, Günther, Christian M, Seng, Boris, Kammerbauer, Fabian, Popescu, Horia, Pancaldi, Matteo, Neeraj, Kumar, Polley, Debanjan, Jangid, Rahul, Hrkac, Stjepan B, Patel, Sheena KK, Ovcharenko, Sergei, Turenne, Diego, Ksenzov, Dmitriy, Boeglin, Christine, Baidakova, Marina, von Korff Schmising, Clemens, Borchert, Martin, Vodungbo, Boris, Chen, Kai, Luo, Chen, Radu, Florin, Müller, Leonard, Flórez, Miriam Martínez, Philippi-Kobs, André, Riepp, Matthias, Roseker, Wojciech, Grübel, Gerhard, Carley, Robert, Schlappa, Justine, Van Kuiken, Benjamin E, Gort, Rafael, Mercadier, Laurent, Agarwal, Naman, Le Guyader, Loïc, Mercurio, Giuseppe, Teichmann, Martin, Delitz, Jan Torben, Reich, Alexander, Broers, Carsten, Hickin, David, Deiter, Carsten, Moore, James, Rompotis, Dimitrios, Wang, Jinxiong, Kane, Daniel, Venkatesan, Sandhya, Meier, Joachim, Pallas, Florent, Jezynski, Tomasz, Lederer, Maximilian, Boukhelef, Djelloul, Szuba, Janusz, Wrona, Krzysztof, Hauf, Steffen, Zhu, Jun, Bergemann, Martin, Kamil, Ebad, Kluyver, Thomas, Rosca, Robert, Spirzewski, Michał, Kuster, Markus, Turcato, Monica, Lomidze, David, Samartsev, Andrey, Engelke, Jan, Porro, Matteo, Maffessanti, Stefano, Hansen, Karsten, Erdinger, Florian, Fischer, Peter, Fiorini, Carlo, Castoldi, Andrea, Manghisoni, Massimo, Wunderer, Cornelia Beatrix, Fullerton, Eric E, Shpyrko, Oleg G, Gutt, Christian, Sanchez-Hanke, Cecilia, Dürr, Hermann A, Iacocca, Ezio, Nembach, Hans T, Keller, Mark W, Shaw, Justin M, Silva, Thomas J, Kukreja, Roopali, Fangohr, Hans, Eisebitt, Stefan, Kläui, Mathias, Jaouen, Nicolas, Scherz, Andreas, Bonetti, Stefano, and Jal, Emmanuelle

Subjects
X-Rays, Lasers, Radiography, Holography, holography, magnetic X-ray scattering, soft X-rays, ultrafast X-ray imaging, XFEL, Condensed Matter Physics, Optical Physics, Physical Chemistry (incl. Structural), Biophysics
Abstract

The advent of X-ray free-electron lasers (XFELs) has revolutionized fundamental science, from atomic to condensed matter physics, from chemistry to biology, giving researchers access to X-rays with unprecedented brightness, coherence and pulse duration. All XFEL facilities built until recently provided X-ray pulses at a relatively low repetition rate, with limited data statistics. Here, results from the first megahertz-repetition-rate X-ray scattering experiments at the Spectroscopy and Coherent Scattering (SCS) instrument of the European XFEL are presented. The experimental capabilities that the SCS instrument offers, resulting from the operation at megahertz repetition rates and the availability of the novel DSSC 2D imaging detector, are illustrated. Time-resolved magnetic X-ray scattering and holographic imaging experiments in solid state samples were chosen as representative, providing an ideal test-bed for operation at megahertz rates. Our results are relevant and applicable to any other non-destructive XFEL experiments in the soft X-ray range.

Published
2022

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