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7,574 results on '"Nithya, A."'

1. Opportunities and Challenges of Generative-AI in Finance

Author: Desai, Akshar Prabhu, Mallya, Ganesh Satish, Luqman, Mohammad, Ravi, Tejasvi, Kota, Nithya, and Yadav, Pranjul
Subjects: Computer Science - Artificial Intelligence
Abstract: Machine Learning and data mining have created widespread impact across various domains. However, these techniques are limited in their ability to reason, understand and generalize w.r.t language specific tasks. The aforementioned challenges were overcome, with the advancement of LLMs/Gen-AI. Gen-AI techniques are able to improve understanding of context and nuances in language modeling, translation between languages, handle large volumes of data, provide fast, low-latency responses and can be fine-tuned for various tasks and domains. In this manuscript, we present a comprehensive overview of the applications of Gen-AI techniques in the finance domain. In particular, we present the opportunities and challenges associated with the usage of Gen-AI techniques in finance. We also illustrate the various methodologies which can be used to train Gen-AI and present the various application areas of Gen-AI techniques in the finance ecosystem. To the best of our knowledge, this work represents the most comprehensive summarization of Gen-AI techniques within the financial domain. The analysis is designed for a deep overview of areas marked for substantial advancement while simultaneously pin-point those warranting future prioritization. We also hope that this work would serve as a conduit between finance and other domains, thus fostering the cross-pollination of innovative concepts and practices.
Published: 2024

2. Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries

Author: Vodrahalli, Kiran, Ontanon, Santiago, Tripuraneni, Nilesh, Xu, Kelvin, Jain, Sanil, Shivanna, Rakesh, Hui, Jeffrey, Dikkala, Nishanth, Kazemi, Mehran, Fatemi, Bahare, Anil, Rohan, Dyer, Ethan, Shakeri, Siamak, Vij, Roopali, Mehta, Harsh, Ramasesh, Vinay, Le, Quoc, Chi, Ed, Lu, Yifeng, Firat, Orhan, Lazaridou, Angeliki, Lespiau, Jean-Baptiste, Attaluri, Nithya, and Olszewska, Kate
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: We introduce Michelangelo: a minimal, synthetic, and unleaked long-context reasoning evaluation for large language models which is also easy to automatically score. This evaluation is derived via a novel, unifying framework for evaluations over arbitrarily long contexts which measure the model's ability to do more than retrieve a single piece of information from its context. The central idea of the Latent Structure Queries framework (LSQ) is to construct tasks which require a model to ``chisel away'' the irrelevant information in the context, revealing a latent structure in the context. To verify a model's understanding of this latent structure, we query the model for details of the structure. Using LSQ, we produce three diagnostic long-context evaluations across code and natural-language domains intended to provide a stronger signal of long-context language model capabilities. We perform evaluations on several state-of-the-art models and demonstrate both that a) the proposed evaluations are high-signal and b) that there is significant room for improvement in synthesizing long-context information.
Published: 2024

3. Hierarchical Generative Modeling of Melodic Vocal Contours in Hindustani Classical Music

Author: Shikarpur, Nithya, Dendukuri, Krishna Maneesha, Wu, Yusong, Caillon, Antoine, and Huang, Cheng-Zhi Anna
Subjects: Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Hindustani music is a performance-driven oral tradition that exhibits the rendition of rich melodic patterns. In this paper, we focus on generative modeling of singers' vocal melodies extracted from audio recordings, as the voice is musically prominent within the tradition. Prior generative work in Hindustani music models melodies as coarse discrete symbols which fails to capture the rich expressive melodic intricacies of singing. Thus, we propose to use a finely quantized pitch contour, as an intermediate representation for hierarchical audio modeling. We propose GaMaDHaNi, a modular two-level hierarchy, consisting of a generative model on pitch contours, and a pitch contour to audio synthesis model. We compare our approach to non-hierarchical audio models and hierarchical models that use a self-supervised intermediate representation, through a listening test and qualitative analysis. We also evaluate audio model's ability to faithfully represent the pitch contour input using Pearson correlation coefficient. By using pitch contours as an intermediate representation, we show that our model may be better equipped to listen and respond to musicians in a human-AI collaborative setting by highlighting two potential interaction use cases (1) primed generation, and (2) coarse pitch conditioning., Comment: Accepted at International Society for Music Information Retrieval (ISMIR) 2024
Published: 2024

4. Characterization of X-ray Detectors in the MIT X-ray Polarimetry Beamline

Author: Heine, Sarah N. T., Marshall, Herman L., Schneider, Benjamin, Lamar, Beverly, Kothnur, Nithya, and Garner, Alan
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The MIT X-ray Polarimetry Beamline is a facility that we developed for testing components for possible use in X-ray polarimetry. Over the past few years, we have demonstrated that the X-ray source can generate nearly 100% polarized X-rays at various energies from 183 eV (Boron K$\alpha$) to 705 eV (Fe L$\alpha$) using a laterally graded multilayer coated mirror (LGML) oriented at 45\deg\ to the source. The position angle of the polarization can be rotated through a range of roughly 150\deg. In a downstream chamber, we can orient a Princeton Instruments MTE1300B CCD camera to observe the polarized light either directly or after reflection at 45\deg\ by a second LGML. In support of the REDSoX Polarimeter project, we have tested four other detectors by directly comparing them to the PI camera. Two were CCD cameras: a Raptor Eagle XV and a CCID94 produced by MIT Lincoln Laboratories, and two had sCMOS sensors: the Sydor Wraith with a GSENSE 400BSI sensor and a custom Sony IMX290 sensor. We will show results comparing quantum efficiencies and event images in the soft X-ray band.
Published: 2024

5. Artificial intelligence to unlock real‐world evidence in clinical oncology: A primer on recent advances

Author: Bryant, Alex K, Zamora‐Resendiz, Rafael, Dai, Xin, Morrow, Destinee, Lin, Yuewei, Jungles, Kassidy M, Rae, James M, Tate, Akshay, Pearson, Ashley N, Jiang, Ralph, Fritsche, Lars, Lawrence, Theodore S, Zou, Weiping, Schipper, Matthew, Ramnath, Nithya, Yoo, Shinjae, Crivelli, Silvia, and Green, Michael D
Subjects: Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Machine Learning and Artificial Intelligence, Clinical Research, Networking and Information Technology R&D (NITRD), Bioengineering, Humans, Artificial Intelligence, Medical Oncology, Neoplasms, Cancer Outcomes Research, Large language models, Observational Data, prognostic factor, Biochemistry and Cell Biology, Oncology and carcinogenesis
Abstract: PurposeReal world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology.MethodsWe performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology.ResultsArtificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping.ConclusionsDespite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.
Published: 2024

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

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

7. SEGAA: A Unified Approach to Predicting Age, Gender, and Emotion in Speech

Author: R, Aron, Sigicharla, Indra, Periwal, Chirag, K, Mohanaprasad, S, Nithya Darisini P, Tiwari, Sourabh, and Arora, Shivani
Subjects: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Computer Science - Sound
Abstract: The interpretation of human voices holds importance across various applications. This study ventures into predicting age, gender, and emotion from vocal cues, a field with vast applications. Voice analysis tech advancements span domains, from improving customer interactions to enhancing healthcare and retail experiences. Discerning emotions aids mental health, while age and gender detection are vital in various contexts. Exploring deep learning models for these predictions involves comparing single, multi-output, and sequential models highlighted in this paper. Sourcing suitable data posed challenges, resulting in the amalgamation of the CREMA-D and EMO-DB datasets. Prior work showed promise in individual predictions, but limited research considered all three variables simultaneously. This paper identifies flaws in an individual model approach and advocates for our novel multi-output learning architecture Speech-based Emotion Gender and Age Analysis (SEGAA) model. The experiments suggest that Multi-output models perform comparably to individual models, efficiently capturing the intricate relationships between variables and speech inputs, all while achieving improved runtime.
Published: 2024

8. Performance evaluation of Cuttlefish optimization-based contention control in Wireless Rechargeable Sensor Network

Author: Susan, T. Siron Anita and Nithya, B.
Published: 2024
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9. Clinical Outcomes of Excisional Goniotomy with the Kahook Dual Blade: 6-Year Results

Author: Vasu, Pranav, Abubaker, Yazan, Boopathiraj, Nithya, Wagner, Isabella V., Lentz, P. Connor, Dorairaj, Emily, Shokair, Aya, Qozat, Ibrahim, Miller, Darby D., and Dorairaj, Syril
Published: 2024
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10. Competitive Adsorption Studies of MgFe2O4-Biochar Nanocomposites for the Removal of Chromium and Nickel Ions in Single and Binary Metal Ion System

Author: Kurup, Gautham, Krishnan, Neeraj, R., Vaishnav M., R., Roopak A., Nithya, K., Sathish, Asha, Sivamani, Selvaraju, and Cheruvally, Aswathy S.
Published: 2024
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11. Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture

Author: Swaminathan, Vaishali, Thomasson, J. Alex, Hardin, Robert G., and Rajan, Nithya
Subjects: Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: Radiometric accuracy of data is crucial in quantitative precision agriculture, to produce reliable and repeatable data for modeling and decision making. The effect of exposure time and gain settings on the radiometric accuracy of multispectral images was not explored enough. The goal of this study was to determine if having a fixed exposure (FE) time during image acquisition improved radiometric accuracy of images, compared to the default auto-exposure (AE) settings. This involved quantifying the errors from auto-exposure and determining ideal exposure values within which radiometric mean absolute percentage error (MAPE) were minimal (< 5%). The results showed that FE orthomosaic was closer to ground-truth (higher R2 and lower MAPE) than AE orthomosaic. An ideal exposure range was determined for capturing canopy and soil objects, without loss of information from under-exposure or saturation from over-exposure. A simulation of errors from AE showed that MAPE < 5% for the blue, green, red, and NIR bands and < 7% for the red edge band for exposure settings within the determined ideal ranges and increased exponentially beyond the ideal exposure upper limit. Further, prediction of total plant nitrogen uptake (g/plant) using vegetation indices (VIs) from two different growing seasons were closer to the ground truth (mostly, R2 > 0.40, and MAPE = 12 to 14%, p < 0.05) when FE was used, compared to the prediction from AE images (mostly, R2 < 0.13, MAPE = 15 to 18%, p >= 0.05).
Published: 2024
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12. Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations

Author: Kulkarni, Pranav, Chan, Andrew, Navarathna, Nithya, Chan, Skylar, Yi, Paul H., and Parekh, Vishwa S.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: The proliferation of artificial intelligence (AI) in radiology has shed light on the risk of deep learning (DL) models exacerbating clinical biases towards vulnerable patient populations. While prior literature has focused on quantifying biases exhibited by trained DL models, demographically targeted adversarial bias attacks on DL models and its implication in the clinical environment remains an underexplored field of research in medical imaging. In this work, we demonstrate that demographically targeted label poisoning attacks can introduce undetectable underdiagnosis bias in DL models. Our results across multiple performance metrics and demographic groups like sex, age, and their intersectional subgroups show that adversarial bias attacks demonstrate high-selectivity for bias in the targeted group by degrading group model performance without impacting overall model performance. Furthermore, our results indicate that adversarial bias attacks result in biased DL models that propagate prediction bias even when evaluated with external datasets., Comment: 29 pages, 4 figures
Published: 2024

13. 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

14. OPTIMAL PRICING AND LOT-SIZE POLICIES FOR INVENTORY MODELS FOR DETERIORATIVE ITEMS WITH BERTRAND AND COURNOTS' MODELS-IN THIRD ORDER EQUATION

Author: Sivashankari, C.K. and Nithya, T.
Published: 2024

15. Novel SEGAA: A Unified Approach to Predicting Age, Gender, and Emotion in Speech

Author: Ritesh, Aron, Sigicharla, Indra Kiran, Periwal, Chirag, Kothandaraman, Mohanaprasad, Darisini, P. S. Nithya, Tiwari, Sourabh, and Arora, Shivani
Published: 2024
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16. Enhanced Electrochemical Performance of the Nanocomposite MnCo2O4 Nanoclusters for Supercapacitors Application

Author: Vanitharaj, S., Balaji, R., Nithya, R., Kavithapriya, D., and Selvakumar, S.
Published: 2024
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17. Hydrothermally grown hollandite manganese dioxide nanorods: evaluation of supercapattery performance and photocatalytic efficiency

Author: George, Nithya S., Ali, Syeda Ramsha, Babu, Ragavendra, Jose, Lolly Maria, Jayavel, R., Sanal, K. C., M P, Harikrishnan, Rimal Isaac, R. S., Sajan, D., and Aravind, Arun
Published: 2024
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18. Role of Bi/Te co-dopants on the thermoelectric properties of SnSe polycrystals: an experimental and theoretical investigation

Author: Shankar, Manasa R., Prabhu, A. N., Ashok, Anuradha M., Davis, Nithya, Srinivasan, Bhuvanesh, and Mishra, Vikash
Published: 2024
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19. A Validated Stability-Indicating RP-HPLC Method for the Simultaneous Estimation of Aloe Emodin, Aloin, and Emodin in a Marketed Formulation of Aloe Vera

Author: Yakkala, Lakshmi Madhuri, Somanjeri, Nithya, and Begum, Shaheen
Published: 2024
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20. Enhancements in Circle Rendering: An Improved Approach to the Midpoint Circle Drawing Algorithm

Author: Nithya, N. S. and Idrisi, M. Javed
Published: 2024
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21. SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras

Author: R, Nithya, S, Malavika, F, Jordan, Gangwar, Arjun, J, Metilda N, Umesh, S, Sarab, Rithik, Dubey, Akhilesh Kumar, Divakaran, Govind, K, Samudra Vijaya, and Gangashetty, Suryakanth V
Subjects: Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: India is home to a multitude of languages of which 22 languages are recognised by the Indian Constitution as official. Building speech based applications for the Indian population is a difficult problem owing to limited data and the number of languages and accents to accommodate. To encourage the language technology community to build speech based applications in Indian languages, we are open sourcing SPRING-INX data which has about 2000 hours of legally sourced and manually transcribed speech data for ASR system building in Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi and Tamil. This endeavor is by SPRING Lab , Indian Institute of Technology Madras and is a part of National Language Translation Mission (NLTM), funded by the Indian Ministry of Electronics and Information Technology (MeitY), Government of India. We describe the data collection and data cleaning process along with the data statistics in this paper., Comment: 3 pages, About SPRING-INX Data
Published: 2023

22. Graph data modelling for outcome prediction in oropharyngeal cancer patients

Author: Bhasker, Nithya, Leger, Stefan, Zwanenburg, Alexander, Reddy, Chethan Babu, Bodenstedt, Sebastian, Löck, Steffen, and Speidel, Stefanie
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. Since patient data is not readily available as a graph, most existing methods either manually define a patient graph, or learn a latent graph based on pairwise similarities between the patients. There are also hypergraph neural network (HGNN)-based methods that were introduced recently to exploit potential higher order associations between the patients by representing them as a hypergraph. In this work, we propose a patient hypergraph network (PHGN), which has been investigated in an inductive learning setup for binary outcome prediction in oropharyngeal cancer (OPC) patients using computed tomography (CT)-based radiomic features for the first time. Additionally, the proposed model was extended to perform time-to-event analyses, and compared with GNN and baseline linear models.
Published: 2023

23. Using microfluidic shear to assess transfusion requirements in trauma patients.

Author: Vuoncino, Leslie, Robles, Anamaria, Barnes, Ashli, Ross, James, Graeff, Leonardo, Anway, Taylor, Vincent, Nico, Tippireddy, Nithya, Tanaka, Kimi, Mays, Randi, and Callcut, Rachael
Subjects: Blood coagulation, Inflammation, blood coagulation tests, blood transfusion
Abstract: BACKGROUND: Viscoelastic assays have widely been used for evaluating coagulopathies but lack the addition of shear stress important to in vivo clot formation. Stasys technology subjects whole blood to shear forces over factor-coated surfaces. Microclot formation is analyzed to determine clot area (CA) and platelet contractile forces (PCFs). We hypothesize the CA and PCF from this novel assay will provide information that correlates with trauma-induced coagulopathy and transfusion requirements. METHODS: Blood samples were collected on adult trauma patients from a single-institution prospective cohort study of high-level activations. Patient and injury characteristics, transfusion data, and outcomes were collected. Thromboelastography, coagulation studies, and Stasys assays were run on paired samples collected at admission. Stasys CA and PCFs were quantified as area under the curve calculations and maximum values. Normal ranges for Stasys assays were determined using healthy donors. Data were compared using Kruskal-Wallis tests and simple linear regression. RESULTS: From March 2021 to January 2023, 108 samples were obtained. Median age was 37.5 (IQR 27.5-52) years; patients were 77% male. 71% suffered blunt trauma, 26% had an Injury Severity Score of ≥25. An elevated international normalized ratio significantly correlated with decreased cumulative PCF (p=0.05), maximum PCF (p=0.05) and CA (p=0.02). Lower cumulative PCF significantly correlated with transfusion of any products at 6 and 24 hours (p=0.04 and p=0.05) as well as packed red blood cells (pRBCs) at 6 and 24 hours (p=0.04 and p=0.03). A decreased maximum PCF showed significant correlation with receiving any transfusion at 6 (p=0.04) and 24 hours (p=0.02) as well as transfusion of pRBCs, fresh frozen plasma, and platelets in the first 6 hours (p=0.03, p=0.03, p=0.03, respectively). CONCLUSIONS: Assessing coagulopathy in real time remains challenging in trauma patients. In this pilot study, we demonstrated that microfluidic approaches incorporating shear stress could predict transfusion requirements at time of admission as well as requirements in the first 24 hours. LEVEL OF EVIDENCE: Level II.
Published: 2024

24. Tumor specimen cold ischemia time impacts molecular cancer drug target discovery

Author: von der Heyde, Silvia, Raman, Nithya, Gabelia, Nina, Matias-Guiu, Xavier, Yoshino, Takayuki, Tsukada, Yuichiro, Melino, Gerry, Marshall, John L., Wellstein, Anton, Juhl, Hartmut, and Landgrebe, Jobst
Published: 2024
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25. A presentation of basal cell adenoma: a case report

Author: Deviprasad Dosemane, Meera Niranjan Khadilkar, Nithya Chandy, and Shikha Jayasheelan
Subjects: Adenoma, Basal cell adenoma, Parotid gland, Parotid neoplasms, Salivary gland neoplasms, Medicine
Abstract: Abstract Background While statistically rare in comparison to other head and neck tumours, parotid gland swellings are often encountered in clinical practice where one of the primary goals of examination becomes distinction between benign and malignant lesions. Hallmarks of malignancy are characterized by a female preponderance, history of radiation exposure, a positive family history, and clinical features like heterogenous consistency, fixity to skin/underlying tissues and involvement of facial nerve. Case presentation Here we present a case of parotid swelling in a 72-year old gentleman from south India that had a curious amalgamation of both benign and malignant features. Conclusions While benign, the risk of malignant transformation and rare multicentric occurrence indicates a need to keep basal cell adenoma in mind in case of parotid swellings and their surgical management.
Published: 2024
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26. Effect of oil pulling on the Streptococcus mutans concentation in plaque around orthodontic brackets -A prospective clinical study

Author: Nithya Jagdish, Sridevi Padmanabhan, and Arun B. Chitharanjan
Subjects: oil pulling, orthodontic brackets, plaque, streptococcus mutans, Dentistry, RK1-715
Abstract: Introduction: Fixed orthodontic appliances create areas of plaque stagnation leading to an increase in the volume, structure, and composition of plaque. This increases the chances of decalcification and white spot lesions. Oil pulling, an ancient practice involving swishing oil in the mouth, has demonstrated a significant reduction in plaque scores after 45 days, and a reduction in salivary Streptococcus mutans concentration in few studies done in nonorthodontic subjects. The aim was to compare the concentration of S. mutans in plaque around orthodontic brackets in patients using oil pulling with sesame oil and those on routine oral hygiene. Subjects and Methods: Twenty subjects requiring fixed orthodontic treatment were divided into two equal groups: Group A-Oil pulling and Group B-Control. All subjects were instructed to follow common oral hygiene methods and in addition, Group A was instructed to perform oil pulling for 30 days starting 1 month after placement of fixed appliances. Plaque specimens were collected from labial surfaces of maxillary lateral incisors and quantification of S. mutans was done using real-time polymerase chain reaction. Mean and standard deviations for descriptive statistics, paired, and unpaired sample t-tests were analyzed. Results: Comparison of S. mutans concentration between T1 and T2 demonstrated a significant difference in both control and study groups. The experimental group showed significantly lesser S. mutans concentration compared to the control group. Conclusions: Oil-pulling therapy with sesame oil resulted in a statistically significant reduction in the concentration of S. mutans in the plaque around orthodontic brackets.
Published: 2024
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27. Bio-materials Used to Enhance Bone Healing in Implant Dentistry – A Review from the Past to Current

Author: B Shri Krishna Prasanth, Akshaya Subhashinee Dhanasekaran, T Renuka, Farhan Khan, Sajida Ali Begum, Srutheesri, Nithya Karpagam, and Rozar Raj B John
Subjects: bio-materials, bone regeneration, dental implants, Biology (General), QH301-705.5
Abstract: An alternating way of replacing the missing teeth involves the use of implants. In implant dentistry, an adequate amount of bone is crucial for the long-term prognosis. Bone volume loss can be caused by congenital, post-traumatic or post-surgical abnormalities, as well as disease processes. In scenarios with a bone defect at a site, where the primary stability of an implant cannot be established or where implant placement is not achievable in the best possible position for subsequent prosthetic therapy, guided bone regeneration before implantation represents the method of choice. During implant placement, a partial discrepancy between the implant’s surface and the socket’s osseous walls frequently leaves the peri-implant region with insufficient bone. It has been shown that barrier membranes can be successfully placed to allow the peri-implant space to be filled with new bone rather than lowering the height of the alveolar ridge to produce a sufficient breadth for implantation.
Published: 2024
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28. Tumor specimen cold ischemia time impacts molecular cancer drug target discovery

Author: Silvia von der Heyde, Nithya Raman, Nina Gabelia, Xavier Matias-Guiu, Takayuki Yoshino, Yuichiro Tsukada, Gerry Melino, John L. Marshall, Anton Wellstein, Hartmut Juhl, and Jobst Landgrebe
Subjects: Cytology, QH573-671
Abstract: Abstract Tumor tissue collections are used to uncover pathways associated with disease outcomes that can also serve as targets for cancer treatment, ideally by comparing the molecular properties of cancer tissues to matching normal tissues. The quality of such collections determines the value of the data and information generated from their analyses including expression and modifications of nucleic acids and proteins. These biomolecules are dysregulated upon ischemia and decompose once the living cells start to decay into inanimate matter. Therefore, ischemia time before final tissue preservation is the most important determinant of the quality of a tissue collection. Here we show the impact of ischemia time on tumor and matching adjacent normal tissue samples for mRNAs in 1664, proteins in 1818, and phosphosites in 1800 cases (tumor and matching normal samples) of four solid tumor types (CRC, HCC, LUAD, and LUSC NSCLC subtypes). In CRC, ischemia times exceeding 15 min impacted 12.5% (mRNA), 25% (protein), and 50% (phosphosites) of differentially expressed molecules in tumor versus normal tissues. This hypoxia- and decay-induced dysregulation increased with longer ischemia times and was observed across tumor types. Interestingly, the proteomics analysis revealed that specimen ischemia time above 15 min is mostly associated with a dysregulation of proteins in the immune-response pathway and less so with metabolic processes. We conclude that ischemia time is a crucial quality parameter for tissue collections used for target discovery and validation in cancer research.
Published: 2024
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29. Investigating the method of selection of background pixel values for the calibration of EBT‐XD film dosimetry

Author: Sathiya Raj, Nithya Shree, and Ganesh Kadirampatti
Subjects: Film dosimetry, EBT‐XD film, Calibration curve, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Abstract Purpose This study investigates three different calibration methods for the selection of background pixel intensity. Methods Film‐by‐Film (FBF) Method: Each film serves as its own control. Batch‐by‐Film (BBF) Method: A single film is used as a control for all calibration films. Generic (GEN) Method: A generic value (65535) is used as the background pixel value for all calibration films.Three calibration curves were established for the red, green, blue, and RGB channels, and the Radbard NIH (image) curve‐fitting model was used to predict the dose. Sensitivity at different dose levels was quantified by calculating the first derivative of each color channel. Results The GEN method exhibited a difference of up to 6% between the predicted and delivered doses below 2 Gy. The changes in optical density when using the GEN method differed significantly (p
Published: 2024
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30. Developing a systems-focused tool for modeling lung cancer screening resource needs

Author: Aparna Reddy, Fumiya Abe-Nornes, Alison Haskell, Momoka Saito, Matthew Schumacher, Advaidh Venkat, Krithika Venkatasubramanian, Kira Woodhouse, Yiran Zhang, Hooman Niktafar, Anthony Leveque, Beth Kedroske, Nithya Ramnath, and Amy Cohn
Subjects: Interventions, Lung cancer screening program, Resource allocations, Screening, Veterans affairs, Medicine (General), R5-920
Abstract: Abstract Background Early detection through screening dramatically improves lung cancer survival rates, including among war Veterans, who are at heightened risk. The effectiveness of low dose computed tomography scans in lung cancer screening (LCS) prompted the Veteran’s Affairs Lung Precision Oncology Program (VA LPOP) to increase screening rates. We aimed to develop an adaptive population health tool to determine adequate resource allocation for the program, with a specific focus on primary care providers, nurse navigators, and radiologists. Methods We developed a tool using C + + that uses inputs that represents the process of the VA LCS program in Ann Arbor, Michigan to calculate FTEs of human resource needs to screen a given population. Further, we performed a sensitivity analysis to understand how resource needs are impacted by changes in population, screening eligibility, and time allocated for the nurse navigators’ tasks. Results Using estimates from the VA LCS Program as demonstrative inputs, we determined that the greatest number of full-time equivalents required were for radiologists, followed by nurse navigators and then primary care providers, for a target population of 75,000. An increase in the population resulted in a linear increase of resource needs, with radiologists experiencing the greatest rate of increase, followed by nurse navigators and primary care providers. These resource requirements changed with primary care providers, nurse navigators and radiologists demonstrating the greatest increase when 1–20, 20–40 and > 40% of Veterans accepted to be screened respectively. Finally, when increasing the time allocated to check eligibility by the nurse navigator from zero to three minutes, there was a linear increase in the full-time equivalents required for the nurse navigator. Conclusion Variation of resource utilization demonstrated by our user facing tool emphasizes the importance of tailored strategies to accommodate specific population demographics and downstream work. We will continue to refine this tool by incorporating additional variability in system parameters, resource requirements following an abnormal test result, and resource distribution over time to reach steady state. While our tool is designed for a specific program in one center, it has wider applicability to other cancer screening programs.
Published: 2024
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31. Field Evaluation of Protein Bait in Attracting the Melon Fruit Fly Zeugodacus cucurbitae (Coquillett) in Snake Gourd

Author: Nithya, K, Kavitha, Zadda, Shanthi, M, Swarnalatha, K, and Mini, M L
Published: 2024
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32. A presentation of basal cell adenoma: a case report

Author: Dosemane, Deviprasad, Khadilkar, Meera Niranjan, Chandy, Nithya, and Jayasheelan, Shikha
Published: 2024
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33. Developing a systems-focused tool for modeling lung cancer screening resource needs

Author: Reddy, Aparna, Abe-Nornes, Fumiya, Haskell, Alison, Saito, Momoka, Schumacher, Matthew, Venkat, Advaidh, Venkatasubramanian, Krithika, Woodhouse, Kira, Zhang, Yiran, Niktafar, Hooman, Leveque, Anthony, Kedroske, Beth, Ramnath, Nithya, and Cohn, Amy
Published: 2024
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34. The value of endocervical curettage during large loop excision of the transformation zone in combination with endocervical surgical margin in predicting persistent/recurrent dysplasia of the uterine cervix: a retrospective study

Author: Scherer-Quenzer, Anne Cathrine, Findeis, Jelena, Herbert, Saskia-Laureen, Yokendren, Nithya, Reinhold, Ann-Kristin, Schlaiss, Tanja, Wöckel, Achim, Diessner, Joachim, and Kiesel, Matthias
Published: 2024
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35. A randomised controlled trial of plasma exchange compared to standard of care in the treatment of severe COVID-19 infection (COVIPLEX)

Author: Arulkumaran, Nishkantha, Thomas, Mari, Stubbs, Matthew, Prasanna, Nithya, Subhan, Maryam, Singh, Deepak, Ambler, Gareth, Waller, Alessia, Singer, Mervyn, Brealey, David, and Scully, Marie
Published: 2024
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36. Prognostic scores for predicting clinical outcomes in upper gastrointestinal bleeding

Author: Khatana, Gaurav, K, Sunil Kumar, Kolassery, Sandesh, Sebastian, Saji, Joseph, Deni, Muraleedharanpillai, Ramu, Joseph, Tony, V, Nithya, Unnikrishnan, Lal Krishna, and Rony, Gino
Published: 2024
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37. Achieving Secured Medical Network (SMN) through Stateless Mechanism and SkeyM in Medical-Internet of Things (M-IoT)

Author: S., Nithya, Palanisamy, SatheeshKumar, and T., Nivethitha
Published: 2024
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38. Current status of community resources and priorities for weed genomics research

Author: Montgomery, Jacob, Morran, Sarah, MacGregor, Dana R., McElroy, J. Scott, Neve, Paul, Neto, Célia, Vila-Aiub, Martin M., Sandoval, Maria Victoria, Menéndez, Analia I., Kreiner, Julia M., Fan, Longjiang, Caicedo, Ana L., Maughan, Peter J., Martins, Bianca Assis Barbosa, Mika, Jagoda, Collavo, Alberto, Merotto, Jr., Aldo, Subramanian, Nithya K., Bagavathiannan, Muthukumar V., Cutti, Luan, Islam, Md. Mazharul, Gill, Bikram S., Cicchillo, Robert, Gast, Roger, Soni, Neeta, Wright, Terry R., Zastrow-Hayes, Gina, May, Gregory, Malone, Jenna M., Sehgal, Deepmala, Kaundun, Shiv Shankhar, Dale, Richard P., Vorster, Barend Juan, Peters, Bodo, Lerchl, Jens, Tranel, Patrick J., Beffa, Roland, Fournier-Level, Alexandre, Jugulam, Mithila, Fengler, Kevin, Llaca, Victor, Patterson, Eric L., and Gaines, Todd A.
Published: 2024
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39. A new multi-criteria decision making method for the selection of construction contractors using interval valued fuzzy set

Author: Nithya, N. S., Thota, Srinivasarao, Rathour, Laxmi, and Shanmugasundaram, P.
Published: 2024
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40. Ablation of Wnt signaling in bone marrow stromal cells overcomes microenvironment-mediated drug resistance in acute myeloid leukemia

Author: Palani, Hamenth Kumar, Ganesan, Saravanan, Balasundaram, Nithya, Venkatraman, Arvind, Korula, Anu, Abraham, Aby, George, Biju, and Mathews, Vikram
Published: 2024
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41. Expression and processing of mature human frataxin after gene therapy in mice

Author: Rojsajjakul, Teerapat, Selvan, Nithya, De, Bishnu, Rosenberg, Jonathan B., Kaminsky, Stephen M., Sondhi, Dolan, Janki, Peter, Crystal, Ronald G., Mesaros, Clementina, Khanna, Richie, and Blair, Ian A.
Published: 2024
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42. Targeting HDAC6 improves anti-CD47 immunotherapy

Author: Gracia-Hernandez, Maria, Yende, Ashutosh S., Gajendran, Nithya, Alahmadi, Zubaydah, Li, Xintang, Munoz, Zuleima, Tan, Karen, Noonepalle, Satish, Shibata, Maho, and Villagra, Alejandro
Published: 2024
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43. Breast cancer detection using enhanced IRI-numerical engine and inverse heat transfer modeling: model description and clinical validation

Author: Gutierrez, Carlos, Owens, Alyssa, Medeiros, Lori, Dabydeen, Donnette, Sritharan, Nithya, Phatak, Pradyumna, and Kandlikar, Satish G.
Published: 2024
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44. Class II HLA-DRB4 is a predictive biomarker for survival following immunotherapy in metastatic non-small cell lung cancer

Author: Jiang, Cindy Y., Zhao, Lili, Green, Michael D., Ravishankar, Shashidhar, Towlerton, Andrea M. H., Scott, Anthony J., Raghavan, Malini, Cusick, Matthew F., Warren, Edus H., and Ramnath, Nithya
Published: 2024
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45. Performance study of sodium alginate (SA) with lithium chloride (LiCl)-based solid-state membrane as an electrolyte in electrochemical device application

Author: Hazaana, S. Aafrin, Ancemma, Joseph, Selvasekarapandian, S., Naachiyar, R. Meera, Balasubramanian, G., Arunkumar, Dorai, Nithya, Hellar, and Vignesh, N. Muniraj
Published: 2024
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46. Some new generalizations of Domination using restrictions on degrees of vertices

Author: Kamath, Shyam S. and Muraleedharan, Nithya
Subjects: Mathematics - Combinatorics, 05C07, 05C69
Abstract: A set $D$ of vertices in a graph $G=(V,E)$ is a degree restricted dominating set for $G$ if each vertex $v_i$ in $D$ is dominating atmost $g(d_i)$ vertices of $V-D$, where $g$ is a function restricting the degree value $d_i$ with respect to the given function value $k_i$ for a natural valued function $f$ from the vertex set of the graph. We define three different types of Degree Restricted Domination by varying the way how the restricted function $g(v_i)$ is defined. If $g(d_i)=\big\lceil \frac{d_i}{k_i}\big\rceil$, the corresponding domination is called the ceil degree restricted domination, in short, $CDRD$, and the dominating set obtained in this manner is the $CDRD$-set. If $g(d_i)=\big\lfloor\frac{d_i}{k_i}\big\rfloor$ or $g(d_i)=d_i-k_i+1$, then the corresponding dominations are respectively called the floor degree restricted domination, in short $FDRD$, or the translate degree restricted domination, $TDRD$. The dominating sets obtained in this manner are the $FDRD$-set and the $TDRD$-set respectively. In this paper, we introduce these new generalizations of the domination number in line with the different $DRD$-sets and study these types of domination for some classes of graphs like complete graphs, caterpillar graphs etc. Degree restricted domination has a vital role in retaining the efficiency of nodes in a network and has many interesting applications., Comment: 9 pages
Published: 2023

47. Therapeutic effectiveness and adverse drug reactions of mirabegron versus solifenacin in the treatment of overactive bladder syndrome

Author: Megha O. Raj, Jinish Jose, Fredrick Paul, Syam Sreedharan, and Nithya Uthaman
Subjects: antimuscarinic agent, beta 3 adrenoreceptor agonist, mirabegron, overactive bladder validated 8-question awareness tool score, overactive bladder syndrome, solifenacin, Medicine, Medicine (General), R5-920
Abstract: Introduction: Overactive bladder (OAB) syndrome is a chronic disease characterized by urinary urgency with or without urge incontinence, frequency, and nocturia and antimuscarinic drugs such as solifenacin have been the mainstay of treatment. Mirabegron a beta 3 adrenoreceptor agonist has recently gained importance in the management of OAB. The rationale of the study is that mirabegron improves the storage function without affecting voiding which increases the therapeutic effectiveness. The objective was to determine the therapeutic effectiveness of mirabegron versus solifenacin. Methods: A prospective observational study was conducted on 298 patients with OAB syndrome attending the urology outpatient department of government medical college after obtaining institutional review board clearance. Patients of both genders, belonging to the 18–65 years of age group, attending the urology outpatient department were selected for the study. Patients were evaluated using the OAB-validated 8-question awareness tool (OAB-V8 score) before and after receiving drugs by direct questionnaire method after receiving informed consent. Patients were prescribed either solifenacin 5 mg or mirabegron 25 mg once daily by the urologist. Follow-up was done after 4 and 12 weeks. Adverse drug reactions of the drugs were assessed using the Central Drug Standard Control Organization suspected adverse reaction (ADR) form, and ADRs were notified to the nearest ADR monitoring center. Results: The mirabegron group showed maximum improvement in the mean OAB-V8 score values from baseline at 4 weeks (12.82 ± 5.86, P < 0.001) and 12 weeks (5.74 ± 3.31, P < 0.001) when compared to solifenacin. OAB-V8 scores of the solifenacin group also showed significant improvement from the baseline at 4 weeks (15.30 ± 5.54, P < 0.001) and 12 weeks (8.05 ± 4.59, P < 0.001). Heart rate, systolic, and diastolic blood pressures did not show significant changes during the follow-up in both the study groups. Thirteen patients developed ADRs such as dry mouth (four patients) and constipation (nine patients) in the solifenacin group. No ADRs were noted in the mirabegron group. Conclusion: Mirabegron showed maximum improvement in the OAB-V8 scores in patients with OAB syndrome, although the solifenacin group also showed improvement. Adverse effects were less in the mirabegron group when compared to the solifenacin group.
Published: 2024
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48. Clinical Outcomes of Excisional Goniotomy with the Kahook Dual Blade: 6-Year Results

Author: Pranav Vasu, Yazan Abubaker, Nithya Boopathiraj, Isabella V. Wagner, P. Connor Lentz, Emily Dorairaj, Aya Shokair, Ibrahim Qozat, Darby D. Miller, and Syril Dorairaj
Subjects: Glaucoma, Goniotomy, Intraocular pressure, IOP-lowering medications, Phacoemulsification, Minimally invasive glaucoma surgery, Ophthalmology, RE1-994
Abstract: Abstract Introduction This study aims to evaluate the long-term clinical outcomes of excisional goniotomy with the Kahook Dual Blade (KDB) in the management of various types of glaucoma. Methods This was a retrospective, noncomparative chart review of 90 eyes of 53 patients with glaucoma that underwent standalone KDB goniotomy (KDB-alone group) or KDB goniotomy with concomitant phacoemulsification (KDB-phaco group) between October 2015 and October 2017. Surgical success was defined as an intraocular pressure (IOP) reduction by ≥ 20% at the last follow-up with no surgical reinterventions required and a final IOP ≥ 4 mmHg and ≤ 21 mmHg. We also report on changes from baseline in IOP, number of glaucoma medications, best-corrected visual acuity (BCVA), and visual field parameters, for up to 72 months. Results At 72 months, mean (standard deviation [SD]) IOP was reduced from 17.5 (5.7) to 13.6 (3.0) mmHg (P
Published: 2024
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49. Evaluating Post-Insertion Complications and Patient Satisfaction of Conventional Complete Dentures: A Retrospective Study

Author: Lavanya Ajay Sharma, Navodika Yaparathna, Nithya Cheruka, Peng Shao, and Menaka Abuzar
Subjects: complete dentures, post-insertion complications, patient satisfaction, Dentistry, RK1-715
Abstract: Conventional complete dentures remain to be an important treatment for edentulous patients, especially those of low socioeconomic status. This study is a retrospective analysis that aims to elucidate influencing factors of post-insertion complications and patient satisfaction. Clinical records of 164 patients who received both maxillary and mandibular complete dentures at an Australian university teaching clinic were assessed and analysed. The parameters considered include reason for attendance, presenting clinical status, experience of operator, numbers of appointments for fabrication and adjustment, post-insertion complications and events, and patient satisfaction. The differences among selected attributes were analysed using a Mann–Whitney test, Chi-square test, or Kruskal–Wallis test where applicable, while the relationships between discrete variables were assessed by Kendall’s correlation. First, the number of appointments for fabrication differed significantly between female (6.8 ± 1.8) and male patients (6.2 ± 1.3; p = 0.025) and between student (6.7 ± 1.7) and professional operators (6.0 ± 1.1; p = 0.015). The number of appointments for adjustment was significantly associated with patient age (τb = 0.167, p = 0.003) and sharply declined after the first three months. Second, post-insertion pain was the most frequent complication, and it was significantly more associated with the mandible than the maxilla (χ2 = 21.670, p = 0.000). Similarly, mandibular complete dentures were also significantly more associated with post-insertion reline than the maxillary (χ2 = 8.804, p = 0.003). Lastly, patient satisfaction was significantly associated with patient attendance with old/worn dentures (χ2 = 9.468, p = 0.002), as well as the absence of retention/stability issues (χ2 = 41.712, p = 0.000), post-insertion reline (χ2 = 29.300, p = 0.000), or denture replacement (χ2 = 13.968, p = 0.000). Dissatisfied patients had a significantly higher age (70.9 ± 11.0 vs. 66.2 ± 12.6; p = 0.023) and number of appointments for adjustment (4.9 ± 3.5 vs. 3.1 ± 2.1; p = 0.001) than satisfied patients. In summary, these findings revealed the above factors that influence the fabrication process, post-insertion complication, and patient satisfaction of conventional complete dentures. Future research may include analysis of cases completed at private practices and consideration of other potentially relevant factors such as denture usage and mental status.
Published: 2024
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50. The value of endocervical curettage during large loop excision of the transformation zone in combination with endocervical surgical margin in predicting persistent/recurrent dysplasia of the uterine cervix: a retrospective study

Author: Anne Cathrine Scherer-Quenzer, Jelena Findeis, Saskia-Laureen Herbert, Nithya Yokendren, Ann-Kristin Reinhold, Tanja Schlaiss, Achim Wöckel, Joachim Diessner, and Matthias Kiesel
Subjects: ECC, Residual disease, Risk of recurrence, Risk factors, Cervical cancer, LLETZ, Gynecology and obstetrics, RG1-991, Public aspects of medicine, RA1-1270
Abstract: Abstract Background Cervical cancer often originates from cervical cell dysplasia. Previous studies mainly focused on surgical margins and high-risk human papillomavirus persistence as factors predicting recurrence. New research highlights the significance of positive findings from endocervical curettage (ECC) during excision treatment. However, the combined influence of surgical margin and ECC status on dysplasia recurrence risk has not been investigated. Methods In this retrospective study, data from 404 women with high-grade squamous intraepithelial lesions (HSIL) who underwent large loop excision of the transformation zone (LLETZ) were analyzed. Records were obtained retrospectively from the hospital’s patient database including information about histopathological finding from ECC, endocervical margin status with orientation of residual disease after LLETZ, recurrent/persistent dysplasia after surgical treatment and need for repeated surgery (LLETZ or hysterectomy). Results Patients with cranial (= endocervical) R1-resection together with cells of HSIL in the ECC experienced re-surgery 17 times. With statistical normal distribution, this would have been expected to happen 5 times (p
Published: 2024
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