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2,311 results on '"Stanczyk A"'

1. Masks and Boxes: Combining the Best of Both Worlds for Multi-Object Tracking

Author

Stanczyk, Tomasz and Bremond, Francois

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multi-object tracking (MOT) involves identifying and consistently tracking objects across video sequences. Traditional tracking-by-detection methods, while effective, often require extensive tuning and lack generalizability. On the other hand, segmentation mask-based methods are more generic but struggle with tracking management, making them unsuitable for MOT. We propose a novel approach, McByte, which incorporates a temporally propagated segmentation mask as a strong association cue within a tracking-by-detection framework. By combining bounding box and mask information, McByte enhances robustness and generalizability without per-sequence tuning. Evaluated on four benchmark datasets - DanceTrack, MOT17, SoccerNet-tracking 2022, and KITTI-tracking - McByte demonstrates performance gain in all cases examined. At the same time, it outperforms existing mask-based methods. Implementation code will be provided upon acceptance.

Published
2024

2. Gemma 2: Improving Open Language Models at a Practical Size

Author

Gemma Team, Riviere, Morgane, Pathak, Shreya, Sessa, Pier Giuseppe, Hardin, Cassidy, Bhupatiraju, Surya, Hussenot, Léonard, Mesnard, Thomas, Shahriari, Bobak, Ramé, Alexandre, Ferret, Johan, Liu, Peter, Tafti, Pouya, Friesen, Abe, Casbon, Michelle, Ramos, Sabela, Kumar, Ravin, Lan, Charline Le, Jerome, Sammy, Tsitsulin, Anton, Vieillard, Nino, Stanczyk, Piotr, Girgin, Sertan, Momchev, Nikola, Hoffman, Matt, Thakoor, Shantanu, Grill, Jean-Bastien, Neyshabur, Behnam, Bachem, Olivier, Walton, Alanna, Severyn, Aliaksei, Parrish, Alicia, Ahmad, Aliya, Hutchison, Allen, Abdagic, Alvin, Carl, Amanda, Shen, Amy, Brock, Andy, Coenen, Andy, Laforge, Anthony, Paterson, Antonia, Bastian, Ben, Piot, Bilal, Wu, Bo, Royal, Brandon, Chen, Charlie, Kumar, Chintu, Perry, Chris, Welty, Chris, Choquette-Choo, Christopher A., Sinopalnikov, Danila, Weinberger, David, Vijaykumar, Dimple, Rogozińska, Dominika, Herbison, Dustin, Bandy, Elisa, Wang, Emma, Noland, Eric, Moreira, Erica, Senter, Evan, Eltyshev, Evgenii, Visin, Francesco, Rasskin, Gabriel, Wei, Gary, Cameron, Glenn, Martins, Gus, Hashemi, Hadi, Klimczak-Plucińska, Hanna, Batra, Harleen, Dhand, Harsh, Nardini, Ivan, Mein, Jacinda, Zhou, Jack, Svensson, James, Stanway, Jeff, Chan, Jetha, Zhou, Jin Peng, Carrasqueira, Joana, Iljazi, Joana, Becker, Jocelyn, Fernandez, Joe, van Amersfoort, Joost, Gordon, Josh, Lipschultz, Josh, Newlan, Josh, Ji, Ju-yeong, Mohamed, Kareem, Badola, Kartikeya, Black, Kat, Millican, Katie, McDonell, Keelin, Nguyen, Kelvin, Sodhia, Kiranbir, Greene, Kish, Sjoesund, Lars Lowe, Usui, Lauren, Sifre, Laurent, Heuermann, Lena, Lago, Leticia, McNealus, Lilly, Soares, Livio Baldini, Kilpatrick, Logan, Dixon, Lucas, Martins, Luciano, Reid, Machel, Singh, Manvinder, Iverson, Mark, Görner, Martin, Velloso, Mat, Wirth, Mateo, Davidow, Matt, Miller, Matt, Rahtz, Matthew, Watson, Matthew, Risdal, Meg, Kazemi, Mehran, Moynihan, Michael, Zhang, Ming, Kahng, Minsuk, Park, Minwoo, Rahman, Mofi, Khatwani, Mohit, Dao, Natalie, Bardoliwalla, Nenshad, Devanathan, Nesh, Dumai, Neta, Chauhan, Nilay, Wahltinez, Oscar, Botarda, Pankil, Barnes, Parker, Barham, Paul, Michel, Paul, Jin, Pengchong, Georgiev, Petko, Culliton, Phil, Kuppala, Pradeep, Comanescu, Ramona, Merhej, Ramona, Jana, Reena, Rokni, Reza Ardeshir, Agarwal, Rishabh, Mullins, Ryan, Saadat, Samaneh, Carthy, Sara Mc, Cogan, Sarah, Perrin, Sarah, Arnold, Sébastien M. R., Krause, Sebastian, Dai, Shengyang, Garg, Shruti, Sheth, Shruti, Ronstrom, Sue, Chan, Susan, Jordan, Timothy, Yu, Ting, Eccles, Tom, Hennigan, Tom, Kocisky, Tomas, Doshi, Tulsee, Jain, Vihan, Yadav, Vikas, Meshram, Vilobh, Dharmadhikari, Vishal, Barkley, Warren, Wei, Wei, Ye, Wenming, Han, Woohyun, Kwon, Woosuk, Xu, Xiang, Shen, Zhe, Gong, Zhitao, Wei, Zichuan, Cotruta, Victor, Kirk, Phoebe, Rao, Anand, Giang, Minh, Peran, Ludovic, Warkentin, Tris, Collins, Eli, Barral, Joelle, Ghahramani, Zoubin, Hadsell, Raia, Sculley, D., Banks, Jeanine, Dragan, Anca, Petrov, Slav, Vinyals, Oriol, Dean, Jeff, Hassabis, Demis, Kavukcuoglu, Koray, Farabet, Clement, Buchatskaya, Elena, Borgeaud, Sebastian, Fiedel, Noah, Joulin, Armand, Kenealy, Kathleen, Dadashi, Robert, and Andreev, Alek

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

Published
2024

3. BOND: Aligning LLMs with Best-of-N Distillation

Author

Sessa, Pier Giuseppe, Dadashi, Robert, Hussenot, Léonard, Ferret, Johan, Vieillard, Nino, Ramé, Alexandre, Shariari, Bobak, Perrin, Sarah, Friesen, Abe, Cideron, Geoffrey, Girgin, Sertan, Stanczyk, Piotr, Michi, Andrea, Sinopalnikov, Danila, Ramos, Sabela, Héliou, Amélie, Severyn, Aliaksei, Hoffman, Matt, Momchev, Nikola, and Bachem, Olivier

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation among N candidates. In this paper, we propose Best-of-N Distillation (BOND), a novel RLHF algorithm that seeks to emulate Best-of-N but without its significant computational overhead at inference time. Specifically, BOND is a distribution matching algorithm that forces the distribution of generations from the policy to get closer to the Best-of-N distribution. We use the Jeffreys divergence (a linear combination of forward and backward KL) to balance between mode-covering and mode-seeking behavior, and derive an iterative formulation that utilizes a moving anchor for efficiency. We demonstrate the effectiveness of our approach and several design choices through experiments on abstractive summarization and Gemma models. Aligning Gemma policies with BOND outperforms other RLHF algorithms by improving results on several benchmarks.

Published
2024

4. 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

5. Progesterone in frozen embryo transfer cycles: assays, circulating concentrations, metabolites, and molecular action

Author

Rachel Mandelbaum, M.D. and Frank Z. Stanczyk, Ph.D.

Subjects
Progesterone, frozen embryo transfer, assisted reproductive technology, assay methodology, metabolites, Diseases of the genitourinary system. Urology, RC870-923, Gynecology and obstetrics, RG1-991
Abstract

Programmed or medicated frozen embryo transfer cycles rely on exogenous progesterone (P) administration to prepare the endometrium for implantation and maintain pregnancy. Presently, the optimal route and dose of P replacement for frozen embryo transfer are not known. In addition, there is a paucity of data and insufficient understanding regarding the metabolism and actions of P in implantation and pregnancy maintenance. In the present review, we discuss how different P assay methodologies affect the determination of P thresholds for implantation and pregnancy maintenance. In addition, we discuss the importance of free P and its regulation in the endometrium and show the complexity of molecular signaling that is required for P-dependent endometrial receptivity. We concluded that future studies should focus on defining accurate circulating and endometrial P concentrations, both for total and free P, and how these concentrations correlate with endometrial receptivity and clinical outcomes.

Published
2024
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6. On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes

Author

Agarwal, Rishabh, Vieillard, Nino, Zhou, Yongchao, Stanczyk, Piotr, Ramos, Sabela, Geist, Matthieu, and Bachem, Olivier

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Knowledge distillation (KD) is widely used for compressing a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, current KD methods for auto-regressive sequence models suffer from distribution mismatch between output sequences seen during training and those generated by the student during inference. To address this issue, we introduce Generalized Knowledge Distillation (GKD). Instead of solely relying on a fixed set of output sequences, GKD trains the student on its self-generated output sequences by leveraging feedback from the teacher on such sequences. Unlike supervised KD approaches, GKD also offers the flexibility to employ alternative loss functions between the student and teacher, which can be useful when the student lacks the expressivity to mimic the teacher's distribution. Furthermore, GKD facilitates the seamless integration of distillation with RL fine-tuning (RLHF). We demonstrate the efficacy of GKD for distilling auto-regressive language models on summarization, translation, and arithmetic reasoning tasks, and task-agnostic distillation for instruction-tuning., Comment: Accepted at ICLR 2024. First two authors contributed equally

Published
2023

7. Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback

Author

Roit, Paul, Ferret, Johan, Shani, Lior, Aharoni, Roee, Cideron, Geoffrey, Dadashi, Robert, Geist, Matthieu, Girgin, Sertan, Hussenot, Léonard, Keller, Orgad, Momchev, Nikola, Ramos, Sabela, Stanczyk, Piotr, Vieillard, Nino, Bachem, Olivier, Elidan, Gal, Hassidim, Avinatan, Pietquin, Olivier, and Szpektor, Idan

Subjects
Computer Science - Computation and Language
Abstract

Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon is emphasized in tasks like summarization, in which the generated summaries should be corroborated by their source article. In this work, we leverage recent progress on textual entailment models to directly address this problem for abstractive summarization systems. We use reinforcement learning with reference-free, textual entailment rewards to optimize for factual consistency and explore the ensuing trade-offs, as improved consistency may come at the cost of less informative or more extractive summaries. Our results, according to both automatic metrics and human evaluation, show that our method considerably improves the faithfulness, salience, and conciseness of the generated summaries., Comment: ACL 2023

Published
2023

8. Genetic influence on vascular smooth muscle cell apoptosis

Author

David G. McVey, Catherine Andreadi, Peng Gong, Paulina J. Stanczyk, Charles U. Solomon, Lenka Turner, Liu Yan, Runji Chen, Junjun Cao, Christopher P. Nelson, John R. Thompson, Haojie Yu, Tom R. Webb, Nilesh J. Samani, and Shu Ye

Subjects
Cytology, QH573-671
Abstract

Abstract Vascular smooth muscle cell (VSMC) proliferation, migration, and apoptosis play important roles in many physiological processes and pathological conditions. To identify genetic influences on VSMC behavior, we measured these traits and undertook genome-wide association studies in primary umbilical artery-derived VSMCs from >2000 individuals. Although there were no genome-wide significant associations for VSMC proliferation or migration, genetic variants at two genomic loci (7p15.3 and 7q32.3) showed highly significant associations with VSMC apoptosis (P = 1.95 × 10−13 and P = 7.47 × 10−9, respectively). The lead variant at the 7p51.3 locus was associated with increased expression of the GSDME and PALS2 genes in VSMCs. Knockdown of GSDME or PALS2 in VSMCs attenuated apoptotic cell death. A protein co-immunoprecipitation assay indicated that GSDME complexed with PALS2. PALS2 knockdown attenuated activated caspase-3 and GSDME fragmentation, whilst GSDME knockdown also reduced activated caspase-3. These findings provide new insights into the genetic regulation of VSMC apoptosis, with potential utility for therapeutic development.

Published
2024
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10. Aperiodic activity differences in individuals with high and low temporal processing efficiency

Author

Klaudia Krystecka, Magdalena Stanczyk, Mikolaj Magnuski, Elzbieta Szelag, and Aneta Szymaszek

Subjects
Temporal information processing, Electroencephalography, Aperiodic activity, Resting state, Gamma oscillations, Cognition, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

It is known that Temporal Information Processing (TIP) underpins our cognitive functioning. Previous research has focused on the relationship between TIP efficiency and oscillatory brain activity, especially the gamma rhythm; however, non-oscillatory (aperiodic or 1/f) brain activity has often been missed. Recent studies have identified the 1/f component as being important for the functioning of the brain. Therefore, the current study aimed to verify whether TIP efficiency is associated with specific EEG resting state cortical activity patterns, including oscillatory and non-oscillatory (aperiodic) brain activities. To measure individual TIP efficiency, we used two behavioral tasks in which the participant judges the order of two sounds separated by millisecond intervals. Based on the above procedure, participants were classified into two groups with high and low TIP efficiency. Using cluster-based permutation analyses, we examined between-group differences in oscillatory and non-oscillatory (aperiodic) components across the 1–90 Hz range. The results revealed that the groups differed in the aperiodic component across the 30–80 Hz range in fronto-central topography. In other words, participants with low TIP efficiency exhibited higher levels of aperiodic activity, and thus a flatter frequency spectrum compared to those with high TIP efficiency. We conclude that participants with low TIP efficiency display higher levels of ‘neural noise’, which is associated with poorer quality and speed of neural processing.

Published
2024
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13. Neuroendocrine, neurotransmitter, and gut microbiota imbalance contributing to potential psychiatric disorder prevalence in polycystic ovarian syndrome

Author

Karis I. Sarkisian, Lananh Ho, B.S., Jane Yang, B.A., Rachel Mandelbaum, M.D., and Frank Z. Stanczyk, Ph.D.

Subjects
Gut-brain axis, neuroendocrine effects, neurotransmitter effects, polycystic ovarian syndrome, psychiatric disorders, Diseases of the genitourinary system. Urology, RC870-923, Gynecology and obstetrics, RG1-991
Abstract

Polycystic ovarian syndrome (PCOS) is the most common endocrine disorder in women, affecting up to 15% of reproductive-aged women. Polycystic ovarian syndrome is a heterogeneous disorder, both in the sense that many different factors may play a role in its manifestation and that multiple systems throughout the body can be affected. Polycystic ovarian syndrome has been linked to an increased prevalence of various psychiatric disorders, including depression and anxiety. Despite the socioeconomic effect that these disorders may have on patients with PCOS and society as a whole, this association is largely lacking in research. There are currently several theories regarding the link between PCOS and mental health. Some suggest that the overactive hypothalamic-pituitary-ovarian and hypothalamic-pituitary-adrenal axes in PCOS patients may alter the hormonal profile and contribute to the development of psychiatric disorders. Other studies speculate that abnormal levels of neurotransmitters and neuronal signaling may play a role. Recently, more research has begun to focus on the gut-brain axis, addressing the nutritional needs of PCOS patients. Studies show that dietary factors such as probiotics and micronutrient supplementation may significantly improve psychiatric symptoms in PCOS patients while helping regulate neurotransmitter levels in the body. In this review, we examine different theories regarding the association between PCOS and psychiatric disorders and point out different areas of research that are needed to broaden our understanding of this association.

Published
2023
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15. RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

Author

Ramos, Sabela, Girgin, Sertan, Hussenot, Léonard, Vincent, Damien, Yakubovich, Hanna, Toyama, Daniel, Gergely, Anita, Stanczyk, Piotr, Marinier, Raphael, Harmsen, Jeremiah, Pietquin, Olivier, and Momchev, Nikola

Subjects
Computer Science - Machine Learning
Abstract

We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning. RLDS enables not only reproducibility of existing research and easy generation of new datasets, but also accelerates novel research. By providing a standard and lossless format of datasets it enables to quickly test new algorithms on a wider range of tasks. The RLDS ecosystem makes it easy to share datasets without any loss of information and to be agnostic to the underlying original format when applying various data processing pipelines to large collections of datasets. Besides, RLDS provides tools for collecting data generated by either synthetic agents or humans, as well as for inspecting and manipulating the collected data. Ultimately, integration with TFDS facilitates the sharing of RL datasets with the research community., Comment: https://github.com/google-research/rlds

Published
2021

19. Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus

Author

Stacey, David, Chen, Lingyan, Stanczyk, Paulina J, Howson, Joanna MM, Mason, Amy M, Burgess, Stephen, MacDonald, Stephen, Langdown, Jonathan, McKinney, Harriett, Downes, Kate, Farahi, Neda, Peters, James E, Basu, Saonli, Pankow, James S, Tang, Weihong, Pankratz, Nathan, Sabater-Lleal, Maria, de Vries, Paul S, Smith, Nicholas L, Gelinas, Amy D, Schneider, Daniel J, Janjic, Nebojsa, Samani, Nilesh J, Ye, Shu, Summers, Charlotte, Chilvers, Edwin R, Danesh, John, and Paul, Dirk S

Subjects
Biological Sciences, Genetics, Prevention, Cardiovascular, Atherosclerosis, Hematology, Heart Disease - Coronary Heart Disease, Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Antigens, CD, Crosses, Genetic, Endothelial Cells, Endothelial Protein C Receptor, Humans, Protein C, Receptors, Cell Surface, Thrombosis, Venous Thromboembolism, CHARGE Hemostasis Working Group
Abstract

Many individual genetic risk loci have been associated with multiple common human diseases. However, the molecular basis of this pleiotropy often remains unclear. We present an integrative approach to reveal the molecular mechanism underlying the PROCR locus, associated with lower coronary artery disease (CAD) risk but higher venous thromboembolism (VTE) risk. We identify PROCR-p.Ser219Gly as the likely causal variant at the locus and protein C as a causal factor. Using genetic analyses, human recall-by-genotype and in vitro experimentation, we demonstrate that PROCR-219Gly increases plasma levels of (activated) protein C through endothelial protein C receptor (EPCR) ectodomain shedding in endothelial cells, attenuating leukocyte-endothelial cell adhesion and vascular inflammation. We also associate PROCR-219Gly with an increased pro-thrombotic state via coagulation factor VII, a ligand of EPCR. Our study, which links PROCR-219Gly to CAD through anti-inflammatory mechanisms and to VTE through pro-thrombotic mechanisms, provides a framework to reveal the mechanisms underlying similar cross-phenotype associations.

Published
2022

20. Launchpad: A Programming Model for Distributed Machine Learning Research

Author

Yang, Fan, Barth-Maron, Gabriel, Stańczyk, Piotr, Hoffman, Matthew, Liu, Siqi, Kroiss, Manuel, Pope, Aedan, and Rrustemi, Alban

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

A major driver behind the success of modern machine learning algorithms has been their ability to process ever-larger amounts of data. As a result, the use of distributed systems in both research and production has become increasingly prevalent as a means to scale to this growing data. At the same time, however, distributing the learning process can drastically complicate the implementation of even simple algorithms. This is especially problematic as many machine learning practitioners are not well-versed in the design of distributed systems, let alone those that have complicated communication topologies. In this work we introduce Launchpad, a programming model that simplifies the process of defining and launching distributed systems that is specifically tailored towards a machine learning audience. We describe our framework, its design philosophy and implementation, and give a number of examples of common learning algorithms whose designs are greatly simplified by this approach.

Published
2021

21. A common timing mechanism across different millisecond domains: evidence from perceptual and motor tasks

Author

Magdalena Stanczyk, Elzbieta Szelag, Klaudia Krystecka, and Aneta Szymaszek

Subjects
Medicine, Science
Abstract

Abstract Temporal information processing (TIP) constitutes a complex construct that underlies many cognitive functions and operates in a few hierarchically ordered time domains. This study aimed to verify the relationship between the tens of milliseconds and hundreds of milliseconds domains, referring to perceptual and motor timing, respectively. Sixty four young healthy individuals participated in this study. They underwent two auditory temporal order judgement tasks to assess their performance in the tens of milliseconds domain; on this basis, groups of high-level performers (HLP) and low-level performers (LLP) were identified. Then, a maximum tapping task was used to evaluate performance in the hundreds of milliseconds domain. The most remarkable result was that HLP achieved a faster tapping rate and synchronised quicker with their “internal clock” during the tapping task than did LLP. This result shows that there is a relationship between accuracy in judging temporally asynchronous stimuli and ability to achieve and maintain the pace of a movement adequate to one’s internal pacemaker. This could indicate the strong contribution of a common timing mechanism, responsible for temporal organisation and coordination of behaviours across different millisecond domains.

Published
2023
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22. Deep Graph Convolutional Networks for Wind Speed Prediction

Author

Stańczyk, Tomasz and Mehrkanoon, Siamak

Subjects
Computer Science - Machine Learning, I.2, I.5
Abstract

Wind speed prediction and forecasting is important for various business and management sectors. In this paper, we introduce new models for wind speed prediction based on graph convolutional networks (GCNs). Given hourly data of several weather variables acquired from multiple weather stations, wind speed values are predicted for multiple time steps ahead. In particular, the weather stations are treated as nodes of a graph whose associated adjacency matrix is learnable. In this way, the network learns the graph spatial structure and determines the strength of relations between the weather stations based on the historical weather data. We add a self-loop connection to the learnt adjacency matrix and normalize the adjacency matrix. We examine two scenarios with the self-loop connection setting (two separate models). In the first scenario, the self-loop connection is imposed as a constant additive. In the second scenario a learnable parameter is included to enable the network to decide about the self-loop connection strength. Furthermore, we incorporate data from multiple time steps with temporal convolution, which together with spatial graph convolution constitutes spatio-temporal graph convolution. We perform experiments on real datasets collected from weather stations located in cities in Denmark and the Netherlands. The numerical experiments show that our proposed models outperform previously developed baseline models on the referenced datasets. We provide additional insights by visualizing learnt adjacency matrices from each layer of our models., Comment: 10 pages, 8 figures

Published
2021

23. SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture

Author

Trebing, Kevin, Stanczyk, Tomasz, and Mehrkanoon, Siamak

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, I.2, I.5
Abstract

Weather forecasting is dominated by numerical weather prediction that tries to model accurately the physical properties of the atmosphere. A downside of numerical weather prediction is that it is lacking the ability for short-term forecasts using the latest available information. By using a data-driven neural network approach we show that it is possible to produce an accurate precipitation nowcast. To this end, we propose SmaAt-UNet, an efficient convolutional neural networks-based on the well known UNet architecture equipped with attention modules and depthwise-separable convolutions. We evaluate our approaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France. The experimental results show that in terms of prediction performance, the proposed model is comparable to other examined models while only using a quarter of the trainable parameters., Comment: 9 pages, 4 figures

Published
2020

24. What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

Author

Andrychowicz, Marcin, Raichuk, Anton, Stańczyk, Piotr, Orsini, Manu, Girgin, Sertan, Marinier, Raphael, Hussenot, Léonard, Geist, Matthieu, Pietquin, Olivier, Michalski, Marcin, Gelly, Sylvain, and Bachem, Olivier

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

In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents. Those choices are usually not extensively discussed in the literature, leading to discrepancy between published descriptions of algorithms and their implementations. This makes it hard to attribute progress in RL and slows down overall progress [Engstrom'20]. As a step towards filling that gap, we implement >50 such ``choices'' in a unified on-policy RL framework, allowing us to investigate their impact in a large-scale empirical study. We train over 250'000 agents in five continuous control environments of different complexity and provide insights and practical recommendations for on-policy training of RL agents.

Published
2020

25. Acme: A Research Framework for Distributed Reinforcement Learning

Author

Hoffman, Matthew W., Shahriari, Bobak, Aslanides, John, Barth-Maron, Gabriel, Momchev, Nikola, Sinopalnikov, Danila, Stańczyk, Piotr, Ramos, Sabela, Raichuk, Anton, Vincent, Damien, Hussenot, Léonard, Dadashi, Robert, Dulac-Arnold, Gabriel, Orsini, Manu, Jacq, Alexis, Ferret, Johan, Vieillard, Nino, Ghasemipour, Seyed Kamyar Seyed, Girgin, Sertan, Pietquin, Olivier, Behbahani, Feryal, Norman, Tamara, Abdolmaleki, Abbas, Cassirer, Albin, Yang, Fan, Baumli, Kate, Henderson, Sarah, Friesen, Abe, Haroun, Ruba, Novikov, Alex, Colmenarejo, Sergio Gómez, Cabi, Serkan, Gulcehre, Caglar, Paine, Tom Le, Srinivasan, Srivatsan, Cowie, Andrew, Wang, Ziyu, Piot, Bilal, and de Freitas, Nando

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying architectures being trained as well as increased complexity of the RL algorithms used to train them. These increases have in turn made it more difficult for researchers to rapidly prototype new ideas or reproduce published RL algorithms. To address these concerns this work describes Acme, a framework for constructing novel RL algorithms that is specifically designed to enable agents that are built using simple, modular components that can be used at various scales of execution. While the primary goal of Acme is to provide a framework for algorithm development, a secondary goal is to provide simple reference implementations of important or state-of-the-art algorithms. These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research. In this work we describe the major design decisions made within Acme and give further details as to how its components can be used to implement various algorithms. Our experiments provide baselines for a number of common and state-of-the-art algorithms as well as showing how these algorithms can be scaled up for much larger and more complex environments. This highlights one of the primary advantages of Acme, namely that it can be used to implement large, distributed RL algorithms that can run at massive scales while still maintaining the inherent readability of that implementation. This work presents a second version of the paper which coincides with an increase in modularity, additional emphasis on offline, imitation and learning from demonstrations algorithms, as well as various new agents implemented as part of Acme., Comment: This work presents a second version of the paper which coincides with an increase in modularity, additional emphasis on offline, imitation and learning from demonstrations algorithms, as well as various new agents implemented as part of Acme

Published
2020

26. Longitudinal antimüllerian hormone and its correlation with pubertal milestones

Author

Smith, Meghan B, Ho, Jacqueline, Ma, Lihong, Lee, Miryoung, Czerwinski, Stefan A, Glenn, Tanya L, Cool, David R, Gagneux, Pascal, Stanczyk, Frank Z, McGinnis, Lynda K, and Lindheim, Steven R

Subjects
Paediatrics, Biomedical and Clinical Sciences, Contraception/Reproduction, Pediatric, Obesity, Clinical Research, Antimüllerian hormone, menarche, pubarche, puberty, thelarche
Abstract

ObjectiveTo examine the changes in AMH levels longitudinally over time and their relationship with both body composition, particularly abdominal adiposity, and milestones of pubertal development in female children.DesignSecondary analysis of a prospective, longitudinal study.SettingUniversity affiliated research center and laboratories.PatientsEighty-nine females were examined between 1990 and 2015 to study child growth and development.InterventionsDemographic, anthropometric, growth, and pubertal milestone data with serum samples stored and subsequently analyzed for AMH.Main outcome measuresLongitudinal change in AMH and predicted AMH levels based on body composition, age, and pubertal milestones including, pubarche, thelarche, and menarche.ResultsNatural log-transformed AMH (AMHlog) levels appeared to have a nonlinear relationship with age, decreasing between 10 and 14 years of age, increasing until 16 years. A mixed effect linear model demonstrated that increased abdominal adiposity (waist/height ratio, WHtR) was significantly associated with the predicted increased AMHlog levels (β=1.37). As females progressed through the Tanner stages, the model predicted decreasing AMHlog values when adjusting for age and WHtR.ConclusionsDeclining AMH levels during puberty may not be reflective of diminished ovarian reserve as observed in adults, but may suggest a permissive role of AMH in the activation of the hypothalamic-pituitary-ovarian axis.

Published
2021

29. SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

Author

Espeholt, Lasse, Marinier, Raphaël, Stanczyk, Piotr, Wang, Ke, and Michalski, Marcin

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

We present a modern scalable reinforcement learning agent called SEED (Scalable, Efficient Deep-RL). By effectively utilizing modern accelerators, we show that it is not only possible to train on millions of frames per second but also to lower the cost of experiments compared to current methods. We achieve this with a simple architecture that features centralized inference and an optimized communication layer. SEED adopts two state of the art distributed algorithms, IMPALA/V-trace (policy gradients) and R2D2 (Q-learning), and is evaluated on Atari-57, DeepMind Lab and Google Research Football. We improve the state of the art on Football and are able to reach state of the art on Atari-57 three times faster in wall-time. For the scenarios we consider, a 40% to 80% cost reduction for running experiments is achieved. The implementation along with experiments is open-sourced so results can be reproduced and novel ideas tried out., Comment: New version that includes changes made during the ICLR 2020 review process (https://openreview.net/forum?id=rkgvXlrKwH)

Published
2019

30. Google Research Football: A Novel Reinforcement Learning Environment

Author

Kurach, Karol, Raichuk, Anton, Stańczyk, Piotr, Zając, Michał, Bachem, Olivier, Espeholt, Lasse, Riquelme, Carlos, Vincent, Damien, Michalski, Marcin, Bousquet, Olivier, and Gelly, Sylvain

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

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner. We introduce the Google Research Football Environment, a new reinforcement learning environment where agents are trained to play football in an advanced, physics-based 3D simulator. The resulting environment is challenging, easy to use and customize, and it is available under a permissive open-source license. In addition, it provides support for multiplayer and multi-agent experiments. We propose three full-game scenarios of varying difficulty with the Football Benchmarks and report baseline results for three commonly used reinforcement algorithms (IMPALA, PPO, and Ape-X DQN). We also provide a diverse set of simpler scenarios with the Football Academy and showcase several promising research directions.

Published
2019

33. Paid-Leave Availability and Public Health and Nutrition Program Participation Following a Birth in the U.S.

Author

Marci Ybarra, Alexandra B. Stanczyk, and Dylan J. F. Bellisle

Subjects
paid leave, SNAP, WIC, Medicaid, mothers and infants, Social Sciences
Abstract

This study examines the relationships between state-provided paid-leave availability and enrollment in public health and nutrition programs (SNAP, Medicaid, WIC) among single low-income women following a birth in the U.S. We hypothesize that women in paid leave states will be less likely to participate in publicly available health and nutrition programs. Data are from the Survey of Income and Program Participation (SIPP), a nationally representative panel survey data set (N = 1168). Descriptive tests of significance and probit regression models are used to examine the relationship between paid-leave availability and participation in SNAP, Medicaid, and WIC following a birth. A descriptive analysis suggests significantly lower enrollment in SNAP but not Medicaid or WIC for single low-income women in paid-leave states compared to those in non-paid-leave states. The finding of significantly lower post-birth SNAP participation in paid-leave states holds in probit models that include potentially relevant mother, household, and state controls.

Published
2024
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34. Estrogenic activity, race/ethnicity, and Indigenous American ancestry among San Francisco Bay Area women

Author

Sanchez, Sylvia S, Tachachartvanich, Phum, Stanczyk, Frank Z, Gomez, Scarlett L, John, Esther M, Smith, Martyn T, and Fejerman, Laura

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Estrogen, Minority Health, Aging, Women's Health, Cancer, Breast Cancer, Health Disparities, Prevention, Adult, Aged, Breast Neoplasms, Case-Control Studies, Estrogens, Ethnicity, Female, Genetic Predisposition to Disease, Humans, Middle Aged, Population Groups, San Francisco, Young Adult, General Science & Technology
Abstract

Estrogens play a significant role in breast cancer development and are not only produced endogenously, but are also mimicked by estrogen-like compounds from environmental exposures. We evaluated associations between estrogenic (E) activity, demographic factors and breast cancer risk factors in Non-Latina Black (NLB), Non-Latina White (NLW), and Latina women. We examined the association between E activity and Indigenous American (IA) ancestry in Latina women. Total E activity was measured with a bioassay in plasma samples of 503 women who served as controls in the San Francisco Bay Area Breast Cancer Study. In the univariate model that included all women with race/ethnicity as the independent predictor, Latinas had 13% lower E activity (p = 0.239) and NLBs had 35% higher activity (p = 0.04) compared to NLWs. In the multivariable model that adjusted for demographic factors, Latinas continued to show lower E activity levels (26%, p = 0.026), but the difference between NLBs and NLWs was no longer statistically significant (p = 0.431). An inverse association was observed between E activity and IA ancestry among Latina women (50% lower in 0% vs. 100% European ancestry, p = 0.027) consistent with our previously reported association between IA ancestry and breast cancer risk. These findings suggest that endogenous estrogens and exogenous estrogen-like compounds that act on the estrogen receptor and modulate E activity may partially explain racial/ethnic differences in breast cancer risk.

Published
2019

35. Publisher Correction: Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus

Author

Stacey, David, Chen, Lingyan, Stanczyk, Paulina J., Howson, Joanna M. M., Mason, Amy M., Burgess, Stephen, MacDonald, Stephen, Langdown, Jonathan, McKinney, Harriett, Downes, Kate, Farahi, Neda, Peters, James E., Basu, Saonli, Pankow, James S., Tang, Weihong, Pankratz, Nathan, Sabater-Lleal, Maria, de Vries, Paul S., Smith, Nicholas L., Gelinas, Amy D., Schneider, Daniel J., Janjic, Nebojsa, Samani, Nilesh J., Ye, Shu, Summers, Charlotte, Chilvers, Edwin R., Danesh, John, and Paul, Dirk S.

Published
2022
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37. Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus

Author

David Stacey, Lingyan Chen, Paulina J. Stanczyk, Joanna M. M. Howson, Amy M. Mason, Stephen Burgess, Stephen MacDonald, Jonathan Langdown, Harriett McKinney, Kate Downes, Neda Farahi, James E. Peters, Saonli Basu, James S. Pankow, Weihong Tang, Nathan Pankratz, Maria Sabater-Lleal, Paul S. de Vries, Nicholas L. Smith, CHARGE Hemostasis Working Group, Amy D. Gelinas, Daniel J. Schneider, Nebojsa Janjic, Nilesh J. Samani, Shu Ye, Charlotte Summers, Edwin R. Chilvers, John Danesh, and Dirk S. Paul

Subjects
Science
Abstract

Many individual genetic risk loci associate with multiple diseases, but the molecular basis of these loci often remains unclear. Here, the authors provide a framework to reveal the genetic cross-disease associations at the PROCR vascular disease locus.

Published
2022
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38. Potential pitfalls of reproductive direct-to-consumer testing

Author

Frank Z. Stanczyk, Ph.D., Rachel S. Mandelbaum, M.D., and Rogerio A. Lobo, M.D.

Subjects
Direct-to-consumer testing, fertility diagnostic testing, ovarian reserve testing, laboratory methodology, Diseases of the genitourinary system. Urology, RC870-923, Gynecology and obstetrics, RG1-991
Abstract

The availability of direct-to-consumer (DTC) testing has dramatically increased over the past 2 decades, particularly those targeted at reproduction and fertility. Several ethical concerns exist with regard to DTC tests, including the lack of governmental regulation and consumer protection, standardized laboratory methodology, and clinical validity and actionability. Physicians must familiarize themselves with the pitfalls of DTC tests to best aid patients in interpreting DTC test results and guide them toward evidence-based treatment plans.

Published
2022
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42. Age as a moderator of the relationship between planning and temporal information processing

Author

Katarzyna Jablonska, Magdalena Stanczyk, Magdalena Piotrowska, Aneta Szymaszek, Barbara Lukomska, Hanna Bednarek, and Elzbieta Szelag

Subjects
Medicine, Science
Abstract

Abstract Planning is a fundamental mental ability related to executive functions. It allows to select, order and execute subgoals to achieve a goal. Studies have indicated that these processes are characterised by a specific temporal dynamics reflected in temporal information processing (TIP) in some tens of millisecond domain. Both planning and TIP decline with age but the underlying mechanisms are unclear. The novel value of the present study was to examine these mechanisms in young (n = 110) and elderly (n = 91) participants in Tower of London task, considering two structural properties of problems: search depth related to static maintenance in working memory, and goal ambiguity reflecting dynamic cognitive flexibility. Results revealed that TIP predicted planning accuracy both directly and indirectly (via preplanning) but only in young participants in problems characterised by high goal ambiguity. Better planning is related to longer preplanning and more efficient TIP. This result demonstrates for the first time age-related differences in the contribution of TIP to planning. In young participants TIP contributed to dynamic cognitive flexibility, but not to static maintenance processes. In elderly such relation was not observed probably because the deficient planning might depend on working memory maintenance rather than on cognitive flexibility.

Published
2022
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43. Low Free Testosterone and Prostate Cancer Risk: A Collaborative Analysis of 20 Prospective Studies

Author

Watts, Eleanor L, Appleby, Paul N, Perez-Cornago, Aurora, Bueno-de-Mesquita, H Bas, Chan, June M, Chen, Chu, Cohn, Barbara A, Cook, Michael B, Flicker, Leon, Freedman, Neal D, Giles, Graham G, Giovannucci, Edward, Gislefoss, Randi E, Hankey, Graeme J, Kaaks, Rudolf, Knekt, Paul, Kolonel, Laurence N, Kubo, Tatsuhiko, Le Marchand, Loïc, Luben, Robert N, Luostarinen, Tapio, Männistö, Satu, Metter, E Jeffrey, Mikami, Kazuya, Milne, Roger L, Ozasa, Kotaro, Platz, Elizabeth A, Quirós, J Ramón, Rissanen, Harri, Sawada, Norie, Stampfer, Meir, Stanczyk, Frank Z, Stattin, Pär, Tamakoshi, Akiko, Tangen, Catherine M, Thompson, Ian M, Tsilidis, Konstantinos K, Tsugane, Shoichiro, Ursin, Giske, Vatten, Lars, Weiss, Noel S, Yeap, Bu B, Allen, Naomi E, Key, Timothy J, and Travis, Ruth C

Subjects
Cancer, Clinical Research, Prevention, Urologic Diseases, Prostate Cancer, Aging, Aetiology, 2.1 Biological and endogenous factors, Adult, Aged, Biomarkers, Case-Control Studies, Down-Regulation, Humans, Male, Middle Aged, Neoplasm Grading, Prospective Studies, Prostatic Neoplasms, Protective Factors, Risk Assessment, Risk Factors, Testosterone, Time Factors, Androgens Pooled analysis, Prospective studies, Prostate cancer, Sex hormones, Epidemiology, Androgens, Pooled analysis, Clinical Sciences, Urology & Nephrology
Abstract

BackgroundExperimental and clinical evidence implicates testosterone in the aetiology of prostate cancer. Variation across the normal range of circulating free testosterone concentrations may not lead to changes in prostate biology, unless circulating concentrations are low. This may also apply to prostate cancer risk, but this has not been investigated in an epidemiological setting.ObjectiveTo examine whether men with low concentrations of circulating free testosterone have a reduced risk of prostate cancer.Design, setting, and participantsAnalysis of individual participant data from 20 prospective studies including 6933 prostate cancer cases, diagnosed on average 6.8 yr after blood collection, and 12 088 controls in the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group.Outcome measurements and statistical analysisOdds ratios (ORs) of incident overall prostate cancer and subtypes by stage and grade, using conditional logistic regression, based on study-specific tenths of calculated free testosterone concentration.Results and limitationsMen in the lowest tenth of free testosterone concentration had a lower risk of overall prostate cancer (OR=0.77, 95% confidence interval [CI] 0.69-0.86; p

Published
2018

44. MRI background parenchymal enhancement, breast density and serum hormones in postmenopausal women.

Author

Brooks, Jennifer, Sung, Janice, Pike, Malcolm, Orlow, Irene, Stanczyk, Frank, Bernstein, Jonine, and Morris, Elizabeth

Subjects
MRI, background parenchymal enhancement, breast cancer, hormones, Biological Availability, Body Mass Index, Breast Density, Contrast Media, Estradiol, Estrone, Female, Humans, Magnetic Resonance Imaging, Middle Aged, Postmenopause, Surveys and Questionnaires
Abstract

Background parenchymal enhancement (BPE) is the degree to which normal breast tissue enhances on contrast-enhanced magnetic resonance imaging (MRI). MRI-density is a volumetric measure of breast density that is highly correlated with mammographic density, an established breast cancer risk factor. Endogenous estrogen concentrations are positively associated with postmenopausal breast cancer risk and BPE has been shown to be sensitive to hormonal exposures. The objective of our study was to examine the relationship between BPE and MRI-density and serum hormone concentrations in postmenopausal women. This was a study of cancer-free postmenopausal women undergoing contrast-enhanced breast MRI (N = 118). At the time of MRI all women completed a self-administered questionnaire and blood samples were collected for hormone analyses. Serum concentrations of estrone (E1), estradiol (E2) and bioavailable E2 were examined by category of BPE and MRI-density. Compared to women with minimal BPE, those who had marked BPE had significantly higher serum concentrations of E1, E2 and bioavailable E2 (90% increase, ptrend across all categories = 0.001; 150% increase, ptrend  = 0.001; and 158% increase, ptrend  = 0.001, respectively). These associations were only affected to a minor extent by adjustment for BMI and other variables. After adjustment for BMI, no significant associations between MRI-density and serum E1, E2 and bioavailable E2 were observed. Serum estrogen concentrations were significantly positively associated with BPE. Our study provides further evidence of the hormone-sensitive nature of BPE, indicating a potential role for BPE as an imaging marker of endogenous and exogenous hormonal exposures in the breast.

Published
2018

45. Neonatal Hormone Concentrations and Risk of Testicular Germ Cell Tumors (TGCT).

Author

Morimoto, Libby M, Zava, David, McGlynn, Katherine A, Stanczyk, Frank Z, Kang, Alice Y, Ma, Xiaomei, Wiemels, Joseph L, and Metayer, Catherine

Subjects
Humans, Neoplasms, Germ Cell and Embryonal, Testicular Neoplasms, Androgens, Estrogens, Chromatography, High Pressure Liquid, Incidence, Risk Factors, Case-Control Studies, Adolescent, Child, Preschool, Infant, Infant, Newborn, United States, Male, Tandem Mass Spectrometry, Pediatric Cancer, Pediatric, Prevention, Rare Diseases, Cancer, Pediatric Research Initiative, Urologic Diseases, Clinical Research, Estrogen, Reproductive health and childbirth, Good Health and Well Being, Medical and Health Sciences, Epidemiology
Abstract

Background: Testicular germ cell tumor (TGCT) incidence has increased over the last 40 years in the United States. In contrast to TGCT among infants, it is hypothesized that TGCT in adolescents and young men is the result of sex steroid hormone imbalance during early fetal development. However, little is known about the neonatal period when abrupt hormonal changes occur, and direct supporting evidence is scarce due to the difficulties in obtaining prediagnostic specimens.Methods: We conducted a population-based case-control study examining hormone levels at birth among 91 infants (0-4 years) and 276 adolescents (15-19 years) diagnosed with TGCT, and 344 matched controls. Estrogen and androgen levels were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) from archived newborn dried blood spots. Logistic regression models were used to estimate the association between each hormone level and TGCT risk.Results: Higher levels of androstenedione were associated with increased TGCT risk among adolescents [odds ratio (OR): 2.33, 95% confidence interval (CI): 1.37-3.97 for highest vs. lowest quartile; P trend = 0.003] but not among infants (OR: 0.70, 95% CI: 0.28-1.77). A similar pattern was observed for testosterone (OR: 1.73, 95% CI: 1.00-3.00,) although the trend was not significant (P trend = 0.12). Associations were stronger among non-Hispanic white subjects, relative to Hispanics. There was no difference by tumor histologic subtype. Estriol (the only detectable estrogen) was not associated with TGCT risk in either age group.Conclusions: Higher levels of neonatal androgens were associated with increased risk of TGCT among adolescents, suggesting that early life hormone levels are related to the later development of TGCT.Impact: This is the first study with direct measures of sex steroid hormones to examine the relationship between estrogens and androgens at birth and risk of adolescent TGCT. Cancer Epidemiol Biomarkers Prev; 27(4); 488-95. ©2018 AACR.

Published
2018

46. Vitamin D Levels, Body Composition, and Metabolic Factors in Asian Indians: Results from the Metabolic Syndrome and Atherosclerosis in South Asians Living in America Pilot Study

Author

Chiang, Janet M, Stanczyk, Frank Z, and Kanaya, Alka M

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Aging, Diabetes, Prevention, Cardiovascular, Complementary and Integrative Health, Obesity, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Aged, Atherosclerosis, Blood Glucose, Blood Pressure, Body Composition, Body Mass Index, Cross-Sectional Studies, Fasting, Female, Humans, India, Intra-Abdominal Fat, Male, Metabolic Syndrome, Middle Aged, Pilot Projects, San Francisco, Sex Factors, Vitamin D, Vitamin D Deficiency, Waist Circumference, Waist-Hip Ratio, 25-OH Vitamin D, Body composition, Metabolism, Cardiovascular disease, Asian Indians, Clinical Sciences, Nutrition & Dietetics, Pediatrics, Medical biochemistry and metabolomics, Nutrition and dietetics
Abstract

Background/aimsAsian Indians have a high prevalence of vitamin D deficiency and metabolic syndrome. Vitamin D deficiency is associated with an increased risk of cardiovascular disease and diabetes.MethodsWe performed a cross-sectional study of 150 Asian Indians (50% male) from the San Francisco Bay Area. We assessed the association between 25-OH vitamin D (25-OHD) levels and vitamin D deficiency with body composition (anthropometric and radiographic measures) and metabolic outcomes.ResultsIn both men and women, the presence of vitamin D deficiency was associated with higher systolic (p = 0.004) and diastolic (p = 0.01) blood pressure, and fasting glucose (p = 0.01). Only in women, vitamin D deficiency status was associated with higher body mass index (BMI), waist-to-hip ratio, visceral fat area, and hepatic fat content after adjusting for age, income, and physical activity level. In women, 25-OHD was also associated with fasting glucose after adjusting for age, income, and physical activity and further adjusting for BMI and waist circumference (β -2.1, 95% CI -0.86 to -0.01, p = 0.04). This association between vitamin D deficiency and metabolic parameters was not significant in men.ConclusionsA lower level of 25-OHD and vitamin D deficiency were associated with higher levels of metabolic factors among Asian Indians. Our findings suggest that 25-OHD metabolism may differ by the distribution of adipose tissue and involve previously unexplored pathways accounting for the variability in the role of vitamin D in cardiovascular disease.

Published
2018

47. Circulating and intraprostatic sex steroid hormonal profiles in relation to male pattern baldness and chest hair density among men diagnosed with localized prostate cancers

Author

Zhou, Cindy Ke, Stanczyk, Frank Z, Hafi, Muhannad, Veneroso, Carmela C, Lynch, Barlow, Falk, Roni T, Niwa, Shelley, Emanuel, Eric, Gao, Yu‐Tang, Hemstreet, George P, Zolfghari, Ladan, Carroll, Peter R, Manyak, Michael J, Sesterhenn, Isabell A, Levine, Paul H, Hsing, Ann W, and Cook, Michael B

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Cancer, Clinical Research, Estrogen, Prostate Cancer, Urologic Diseases, Aetiology, 2.2 Factors relating to the physical environment, Aged, Aged, 80 and over, Alopecia, Biomarkers, Follow-Up Studies, Gonadal Steroid Hormones, Hair, Hair Follicle, Humans, Male, Middle Aged, Pilot Projects, Prostatic Neoplasms, Thorax, chest hair density, male pattern baldness, prostate tissue, serum, sex steroid hormones, Paediatrics and Reproductive Medicine, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundProspective cohort studies of circulating sex steroid hormones and prostate cancer risk have not provided a consistent association, despite evidence from animal and clinical studies. However, studies using male pattern baldness as a proxy of early-life or cumulative androgen exposure have reported significant associations with aggressive and fatal prostate cancer risk. Given that androgens underlie the development of patterned hair loss and chest hair, we assessed whether these two dermatological characteristics were associated with circulating and intraprostatic concentrations of sex steroid hormones among men diagnosed with localized prostate cancer.MethodsWe included 248 prostate cancer patients from the NCI Prostate Tissue Study, who answered surveys and provided a pre-treatment blood sample as well as fresh frozen adjacent normal prostate tissue. Male pattern baldness and chest hair density were assessed by trained nurses before surgery. General linear models estimated geometric means and 95% confidence intervals (95%CIs) of each hormone variable by dermatological phenotype with adjustment for potential confounding variables. Subgroup analyses were performed by Gleason score (

Published
2017

49. Refining genotype–phenotype correlations in 304 patients with autosomal recessive polycystic kidney disease and PKHD1 gene variants

Author

Eid, Loai Akram, Arbeiter, Klaus, Godefroid, Nathalie, Lombet, Jacques, De Mul, Aurélie, Feldkoetter, Markus, Zieg, Jakub, Grundmann, Franziska, Galiano, Matthias, Buchholz, Björn, Buescher, Anja, Häffner, Karsten, Gross, Oliver, Patzer, Ludwig, Oh, Jun, Haffner, Dieter, Bernhardt, Wanja, Schaefer, Susanne, Wygoda, Simone, Halbritter, Jan, Derichs, Ute, Klaus, Günter, Lechner, Felix, Ponsel, Sabine, König, Jens, Staude, Hagen, Wurm, Donald, Bald, Martin, Gessner, Michaela, Soliman, Neveen A., Ariceta, Gema, Gonzalez Rodriguez, Juan David, Ojeda, Francisco de la Cerda, Harambat, Jerome, Morin, Denis, Dossier, Claire, Dorval, Guillaume, Shroff, Rukshana, Stabouli, Stella, Hooman, Nakysa, Mencarelli, Francesca, Morello, William, Longo, Germana, Emma, Francesco, Jankauskiene, Augustina, Taranta-Janusz, Katarzyna, Zagozdzon, Ilona, Zachwieja, Katarzyna, Stanczyk, Malgorzata, Bienias, Beata, Litwin, Mieczyslaw, Morawiec-Knysak, Aurelia, Afonso, Alberto Caldas, Dunand, Oliver, Rachisan, Andreea, Miloševski-Lomić, Gordana, Papizh, Svetlana, Rus, Rina, Jilani, Houweyda, Atmis, Bahriye, Duzova, Ali, Soylu, Alper, Candan, Cengiz, Caliskan, Salim, Yilmaz, Alev, Gökce, İbrahim, Akinci, Nurver, Mir, Sevgi, Dursun, Ismail, Tabel, Yilmaz, Nalcacioglu, Hulya, Burgmaier, Kathrin, Brinker, Leonie, Erger, Florian, Beck, Bodo B., Benz, Marcus R., Bergmann, Carsten, Boyer, Olivia, Collard, Laure, Dafinger, Claudia, Fila, Marc, Kowalewska, Claudia, Lange-Sperandio, Bärbel, Massella, Laura, Mastrangelo, Antonio, Mekahli, Djalila, Miklaszewska, Monika, Ortiz-Bruechle, Nadina, Prikhodina, Larisa, Ranchin, Bruno, Ranguelov, Nadejda, Schild, Raphael, Seeman, Tomas, Sever, Lale, Sikora, Przemyslaw, Szczepanska, Maria, Teixeira, Ana, Thumfart, Julia, Uetz, Barbara, Weber, Lutz Thorsten, Wühl, Elke, Zerres, Klaus, Dötsch, Jörg, Schaefer, Franz, and Liebau, Max Christoph

Published
2021
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