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1. Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers

Author

Hwang, Sukjun, Lahoti, Aakash, Dao, Tri, and Gu, Albert

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

A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers. This paper studies a unifying matrix mixer view of sequence mixers that can be conceptualized as a linear map on the input sequence. This framework encompasses a broad range of well-known sequence models, including the self-attention of Transformers as well as recent strong alternatives such as structured state space models (SSMs), and allows understanding downstream characteristics such as efficiency and expressivity through properties of their structured matrix class. We identify a key axis of matrix parameterizations termed sequence alignment, which increases the flexibility and performance of matrix mixers, providing insights into the strong performance of Transformers and recent SSMs such as Mamba. Furthermore, the matrix mixer framework offers a systematic approach to developing sequence mixers with desired properties, allowing us to develop several new sub-quadratic sequence models. In particular, we propose a natural bidirectional extension of the Mamba model (Hydra), parameterized as a quasiseparable matrix mixer, which demonstrates superior performance over other sequence models including Transformers on non-causal tasks. As a drop-in replacement for attention layers, Hydra outperforms BERT by 0.8 points on the GLUE benchmark and ViT by 2% Top-1 accuracy on ImageNet.

Published
2024

2. Automated Adversarial Discovery for Safety Classifiers

Author

Lal, Yash Kumar, Lahoti, Preethi, Sinha, Aradhana, Qin, Yao, and Balashankar, Ananth

Subjects
Computer Science - Computation and Language
Abstract

Safety classifiers are critical in mitigating toxicity on online forums such as social media and in chatbots. Still, they continue to be vulnerable to emergent, and often innumerable, adversarial attacks. Traditional automated adversarial data generation methods, however, tend to produce attacks that are not diverse, but variations of previously observed harm types. We formalize the task of automated adversarial discovery for safety classifiers - to find new attacks along previously unseen harm dimensions that expose new weaknesses in the classifier. We measure progress on this task along two key axes (1) adversarial success: does the attack fool the classifier? and (2) dimensional diversity: does the attack represent a previously unseen harm type? Our evaluation of existing attack generation methods on the CivilComments toxicity task reveals their limitations: Word perturbation attacks fail to fool classifiers, while prompt-based LLM attacks have more adversarial success, but lack dimensional diversity. Even our best-performing prompt-based method finds new successful attacks on unseen harm dimensions of attacks only 5\% of the time. Automatically finding new harmful dimensions of attack is crucial and there is substantial headroom for future research on our new task., Comment: Published at Fourth Workshop on TrustworthyNLP (TrustNLP) at NAACL 2024

Published
2024

3. Inducing Group Fairness in Prompt-Based Language Model Decisions

Author

Atwood, James, Scherrer, Nino, Lahoti, Preethi, Balashankar, Ananth, Prost, Flavien, and Beirami, Ahmad

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

Classifiers are used throughout industry to enforce policies, ranging from the detection of toxic content to age-appropriate content filtering. While these classifiers serve important functions, it is also essential that they are built in ways that minimize unfair biases for users. One such fairness consideration is called group fairness, which desires that different sub-population of users receive equal treatment. This is a well-studied problem in the context of 'classical' classifiers. However, the emergence of prompt-based language model (LM) decision making has created new opportunities to solve text-based classification tasks, and the fairness properties of these new classifiers are not yet well understood. Further, the `remediation toolkit' is incomplete for LM-based decision makers and little is understood about how to improve decision maker group fairness while maintaining classifier performance. This work sets out to add more tools to that toolbox. We introduce adaptations of existing effective approaches from the classical classifier fairness to the prompt-based classifier space. We also devise simple methods that take advantage of the new structure of prompt-based decision makers and operate at the prompt level. We compare these approaches empirically on real data. Our results suggest that adaptations of approaches that are effective for classical classifiers remain effective in the LM-based classifier environment. However, there is room for further exploration of prompt-based remediation methods (and other remediation methods that take advantage of LM structure).

Published
2024

5. Revisiting the sustainability science research agenda

Author

Sahle, Mesfin, Lahoti, Shruti Ashish, Lee, So-Young, Brundiers, Katja, van Riper, Carena J., Pohl, Christian, Chien, Herlin, Bohnet, Iris C., Aguilar-Rivera, Noé, Edwards, Peter, Pradhan, Prajal, Plieninger, Tobias, Boonstra, Wiebren Johannes, Flor, Alexander G., Di Fabio, Annamaria, Scheidel, Arnim, Gordon, Chris, Abson, David J., Andersson, Erik, Demaria, Federico, Kenter, Jasper O., Brooks, Jeremy, Kauffman, Joanne, Hamann, Maike, Graziano, Martin, Nagabhatla, Nidhi, Mimura, Nobuo, Fagerholm, Nora, O’Farrell, Patrick, Saito, Osamu, and Takeuchi, Kazuhiko

Published
2024
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6. Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs

Author

Lahoti, Aakash, Karp, Stefani, Winston, Ezra, Singh, Aarti, and Li, Yuanzhi

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

Vision tasks are characterized by the properties of locality and translation invariance. The superior performance of convolutional neural networks (CNNs) on these tasks is widely attributed to the inductive bias of locality and weight sharing baked into their architecture. Existing attempts to quantify the statistical benefits of these biases in CNNs over locally connected convolutional neural networks (LCNs) and fully connected neural networks (FCNs) fall into one of the following categories: either they disregard the optimizer and only provide uniform convergence upper bounds with no separating lower bounds, or they consider simplistic tasks that do not truly mirror the locality and translation invariance as found in real-world vision tasks. To address these deficiencies, we introduce the Dynamic Signal Distribution (DSD) classification task that models an image as consisting of $k$ patches, each of dimension $d$, and the label is determined by a $d$-sparse signal vector that can freely appear in any one of the $k$ patches. On this task, for any orthogonally equivariant algorithm like gradient descent, we prove that CNNs require $\tilde{O}(k+d)$ samples, whereas LCNs require $\Omega(kd)$ samples, establishing the statistical advantages of weight sharing in translation invariant tasks. Furthermore, LCNs need $\tilde{O}(k(k+d))$ samples, compared to $\Omega(k^2d)$ samples for FCNs, showcasing the benefits of locality in local tasks. Additionally, we develop information theoretic tools for analyzing randomized algorithms, which may be of interest for statistical research., Comment: 40 pages, 4 figures, Accepted to ICLR 2024, Spotlight

Published
2024

10. 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

11. FRAPPE: A Group Fairness Framework for Post-Processing Everything

Author

Tifrea, Alexandru, Lahoti, Preethi, Packer, Ben, Halpern, Yoni, Beirami, Ahmad, and Prost, Flavien

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model. In these situations, post-processing is a viable alternative. However, current methods are tailored to specific problem settings and fairness definitions and hence, are not as broadly applicable as in-processing. In this work, we propose a framework that turns any regularized in-processing method into a post-processing approach. This procedure prescribes a way to obtain post-processing techniques for a much broader range of problem settings than the prior post-processing literature. We show theoretically and through extensive experiments that our framework preserves the good fairness-error trade-offs achieved with in-processing and can improve over the effectiveness of prior post-processing methods. Finally, we demonstrate several advantages of a modular mitigation strategy that disentangles the training of the prediction model from the fairness mitigation, including better performance on tasks with partial group labels., Comment: Conference paper at ICML 2024

Published
2023

12. AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications

Author

Radharapu, Bhaktipriya, Robinson, Kevin, Aroyo, Lora, and Lahoti, Preethi

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

Adversarial testing of large language models (LLMs) is crucial for their safe and responsible deployment. We introduce a novel approach for automated generation of adversarial evaluation datasets to test the safety of LLM generations on new downstream applications. We call it AI-assisted Red-Teaming (AART) - an automated alternative to current manual red-teaming efforts. AART offers a data generation and augmentation pipeline of reusable and customizable recipes that reduce human effort significantly and enable integration of adversarial testing earlier in new product development. AART generates evaluation datasets with high diversity of content characteristics critical for effective adversarial testing (e.g. sensitive and harmful concepts, specific to a wide range of cultural and geographic regions and application scenarios). The data generation is steered by AI-assisted recipes to define, scope and prioritize diversity within the application context. This feeds into a structured LLM-generation process that scales up evaluation priorities. Compared to some state-of-the-art tools, AART shows promising results in terms of concept coverage and data quality.

Published
2023

13. Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting

Author

Lahoti, Preethi, Blumm, Nicholas, Ma, Xiao, Kotikalapudi, Raghavendra, Potluri, Sahitya, Tan, Qijun, Srinivasan, Hansa, Packer, Ben, Beirami, Ahmad, Beutel, Alex, and Chen, Jilin

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

A crucial challenge for generative large language models (LLMs) is diversity: when a user's prompt is under-specified, models may follow implicit assumptions while generating a response, which may result in homogenization of the responses, as well as certain demographic groups being under-represented or even erased from the generated responses. In this paper, we formalize diversity of representation in generative LLMs. We present evaluation datasets and propose metrics to measure diversity in generated responses along people and culture axes. We find that LLMs understand the notion of diversity, and that they can reason and critique their own responses for that goal. This finding motivated a new prompting technique called collective-critique and self-voting (CCSV) to self-improve people diversity of LLMs by tapping into its diversity reasoning capabilities, without relying on handcrafted examples or prompt tuning. Extensive empirical experiments with both human and automated evaluations show that our proposed approach is effective at improving people and culture diversity, and outperforms all baseline methods by a large margin., Comment: To appear at EMNLP 2023 main conference

Published
2023

14. Impact of Loss-Framing and Risk Attitudes on Insurance Purchase: Insights from a Game-like Interface Study

Author

Lahoti, Kunal Rajesh, Hanji, Shivani, Kamble, Pratik, and Vemuri, Kavita

Subjects
Economics - General Economics
Abstract

This study investigates the impact of loss-framing and individual risk attitude on willingness- to purchase insurance products utilizing a game-like interface as choice architecture. The application presents events as experienced in real life. Both financial and emotional loss-framing events are followed by choices to purchase insurance. The participant cohorts considered were undergraduate students and older participants; the latter group was further subdivided by income and education. The within-subject analysis reveals that the loss framing effect on insurance consumption is higher in the younger population, though contingent on the insurance product type. Health and accident insurance shows a negative correlation with risk attitudes for younger participants and a positive correlation with accident insurance for older participants. Risk attitude and life insurance products showed no dependency. The findings elucidate the role of age, income, family responsibilities, and risk attitude in purchasing insurance products. Importantly, it confirms the heuristics of framing/nudging.

Published
2023

20. Sharpened Lazy Incremental Quasi-Newton Method

Author

Lahoti, Aakash, Senapati, Spandan, Rajawat, Ketan, and Koppel, Alec

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

The problem of minimizing the sum of $n$ functions in $d$ dimensions is ubiquitous in machine learning and statistics. In many applications where the number of observations $n$ is large, it is necessary to use incremental or stochastic methods, as their per-iteration cost is independent of $n$. Of these, Quasi-Newton (QN) methods strike a balance between the per-iteration cost and the convergence rate. Specifically, they exhibit a superlinear rate with $O(d^2)$ cost in contrast to the linear rate of first-order methods with $O(d)$ cost and the quadratic rate of second-order methods with $O(d^3)$ cost. However, existing incremental methods have notable shortcomings: Incremental Quasi-Newton (IQN) only exhibits asymptotic superlinear convergence. In contrast, Incremental Greedy BFGS (IGS) offers explicit superlinear convergence but suffers from poor empirical performance and has a per-iteration cost of $O(d^3)$. To address these issues, we introduce the Sharpened Lazy Incremental Quasi-Newton Method (SLIQN) that achieves the best of both worlds: an explicit superlinear convergence rate, and superior empirical performance at a per-iteration $O(d^2)$ cost. SLIQN features two key changes: first, it incorporates a hybrid strategy of using both classic and greedy BFGS updates, allowing it to empirically outperform both IQN and IGS. Second, it employs a clever constant multiplicative factor along with a lazy propagation strategy, which enables it to have a cost of $O(d^2)$. Additionally, our experiments demonstrate the superiority of SLIQN over other incremental and stochastic Quasi-Newton variants and establish its competitiveness with second-order incremental methods., Comment: 36 pages, 2 figures; Accepted to AISTATS 2024

Published
2023

23. FreeMove: Mobile Dependence Initiative

Author

Ingale, Kiran, Sonone, Swati, Yalmate, Ketan, Lahoti, Yash, Yadav, Tanmay, Wagh, Rahul, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Lanka, Surekha, editor, Sarasa-Cabezuelo, Antonio, editor, and Tugui, Alexandru, editor

Published
2024
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24. Evaluation of Ondansetron Hydrochloride Interactions with Mannitol

Author

Khandekar, Mangala J., Lahoti, Swarup R., Kulkarni, Ravindra G., Choudhary, Aasiya N., Pawar, Prashant M., editor, Ronge, Babruvahan P., editor, Gidde, Ranjitsinha R., editor, Pawar, Meenakshi M., editor, Misal, Nitin D., editor, Budhewar, Anupama S., editor, More, Vrunal V., editor, and Reddy, P. Venkata, editor

Published
2024
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26. Effects of aircraft operating fluids and environmental thermal fatigue on fly ash and steel slag based cementitious composites

Author

Aniruddha Tangirala, S. Rawat, and Mukund Lahoti

Subjects
Environmental thermal fatigue, Airfield pavement, High-volume fly ash, Steel slag fine aggregates, Aircraft fluids, Medicine, Science
Abstract

Abstract This paper investigates the performance of concrete incorporating high-volume fly ash (HVFA) and steel slag aggregates against the detrimental effects of combined cycles of environmental thermal fatigue and exposure to leaked aircraft fluids. A total of 128 cubes and 90 prisms were cast for five mixes and exposed to 60, 120, 180, 240 and 300 combined cycles. The results demonstrate the positive effect of utilization of HVFA which reduces the total amount of portlandite available in the system. The SS aggregates demonstrate a strong interlocking with the surrounding matrix and supply the necessary portlandite for continued pozzolanic reaction. However, their reaction with aircraft fluids causes significant degradation to flexural strength initially, which is redeemed by pozzolanic reaction at a later stage. Hybrid basalt and polypropylene fibres were successful in enhancing the flexural strength and reducing the cracking. The mercury intrusion porosimetry revealed a reduction in pore volume because of HVFA. Scanning electron microscopy and differential scanning calorimetry were also employed to uncover the underlying mechanisms of damage and assess the performance of the cementitious composite.

Published
2024
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27. A mixed methods evaluation of the impact of ECHO® telementoring model for capacity building of community health workers in India

Author

Rajmohan Panda, Supriya Lahoti, Nivedita Mishra, Rajath R. Prabhu, Kalpana Singh, Apoorva Karan Rai, and Kumud Rai

Subjects
Community health workers (CHWs), Accredited social health activists (ASHAs), Maternal and child health, Primary healthcare, Health worker training, ECHO telementoring, Medicine (General), R5-920, Public aspects of medicine, RA1-1270
Abstract

Abstract Introduction India has the largest cohort of community health workers with one million Accredited Social Health Activists (ASHAs). ASHAs play vital role in providing health education and promoting accessible health care services in the community. Despite their potential to improve the health status of people, they remain largely underutilized because of their limited knowledge and skills. Considering this gap, Extension for Community Healthcare Outcomes (ECHO)® India, in collaboration with the National Health System Resource Centre (NHSRC), implemented a 15-h (over 6 months) refresher training for ASHAs using a telementoring interface. The present study intends to assess the impact of the training program for improving the knowledge and skills of ASHA workers. Methods We conducted a pre–post quasi-experimental study using a convergent parallel mixed-method approach. The quantitative survey (n = 490) assessed learning competence, performance, and satisfaction of the ASHAs. In addition to the above, in-depth interviews with ASHAs (n = 12) and key informant interviews with other stakeholders (n = 9) examined the experience and practical applications of the training. Inferences from the quantitative and qualitative approaches were integrated during the reporting stage and presented using an adapted Moore’s Expanded Outcomes Framework. Results There was a statistically significant improvement in learning (p = 0.038) and competence (p = 0.01) after attending the training. Participants were satisfied with the opportunity provided by the teleECHO™ sessions to upgrade their knowledge. However, internet connectivity, duration and number of participants in the sessions were identified as areas that needed improvement for future training programs. An improvement in confidence to communicate more effectively with the community was reported. Positive changes in the attitudes of ASHAs towards patient and community members were also reported after attending the training. The peer-to-peer learning through case-based discussion approach helped ensure that the training was relevant to the needs and work of the ASHAs. Conclusions The ECHO Model ™ was found effective in improving and updating the knowledge and skills of ASHAs across different geographies in India. Efforts directed towards knowledge upgradation of ASHAs are crucial for strengthening the health system at the community level. The findings of this study can be used to guide future training programs. Trial registration The study has been registered at the Clinical Trials Registry, India (CTRI/2021/10/037189) dated 08/10/2021.

Published
2024
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28. Colour Stability of Two Different Denture Base Resins and the Efficacy of Two Denture Cleansing Agents after Tea and Coffee Staining: An In-vitro Study

Author

Sonam S Agrawal, Monal M Kukde, Krishna Kumar S Lahoti, Jaykumar R Gade, and Nandkishor J Bankar

Subjects
clinsodent, colourimeter, fittydent, polymethylmethacrylate denture base resin, stains, Medicine
Abstract

Introduction: In dentistry, colour is one of the most important dimensions of aesthetics. Due to the diverse food habits in India, stains accumulate on dentures. Various denture cleansing agents such as Fittydent and Clinsodent are widely used, emphasising the importance of patient oral hygiene. Aim: To evaluate the colour stability of heat-cure activated acrylic denture base resins {Dental Products of India (DPI) and Trevalon} after staining with tea and coffee, subsequently treated with Clinsodent, Fittydent, and distilled water (control group) as cleansing agents. Materials and Methods: This in-vitro study was conducted at Swargiya Dadasaheb Kalmegh Smruti Dental College and Hospital, Nagpur, in the Department of Prosthodontics, Crown and Bridge, and Implantology in collaboration with Indorama, Nagpur, Maharashtra, India, over a period of approximately 90 days (August-October 2017). Total 120 samples were prepared in-vitro study using custom-made brass metal discs measuring 10 mm by 2 mm. Trevalon and DPI heat-cure acrylic denture base resins were used for sample preparation. Baseline colour stability values were established after immersing the samples in distilled water at 37°C for 24 hours. The samples were then stained for eight hours, followed by immersion in different cleansing agents and distilled water for 12 hours. This process was repeated every 24 hours for 60 days, with the staining and cleansing agents replenished daily. Colour measurements were taken at baseline, 15th, 30th, 45th, and 60th days. Statistical analysis involved t-tests and one-way Analysis of Variance (ANOVA). Results: The DPI denture base resin exhibited a highly significant ΔE (change in energy) at 45 days (p=0.0001), while Trevalon showed significance at 60 days (p=0.0079). The difference in means was statistically significant using t-tests for independent samples, and the variance-measure analysis indicated statistical significance in the means of ΔE. The comparison of the three cleansing agents at 60 days was statistically obtained using one-way ANOVA. Conclusion: Dental Products of India (DPI) showed more colour variation than Trevalon. Tea showed a higher staining than coffee. Staining intensity increased over time, peaking at 45 days and stabilising thereafter. Clinsodent is better than Fittydent in removing stains.

Published
2024
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29. TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data

Author

Chundawat, Vikram S, Tarun, Ayush K, Mandal, Murari, Lahoti, Mukund, and Narang, Pratik

Subjects
Computer Science - Machine Learning
Abstract

Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality synthetic data is challenging. Several probabilistic, statistical, generative adversarial networks (GANs), and variational auto-encoder (VAEs) based approaches have been presented for synthetic tabular data generation. Once generated, evaluating the quality of the synthetic data is quite challenging. Some of the traditional metrics have been used in the literature but there is lack of a common, robust, and single metric. This makes it difficult to properly compare the effectiveness of different synthetic tabular data generation methods. In this paper we propose a new universal metric, TabSynDex, for robust evaluation of synthetic data. The proposed metric assesses the similarity of synthetic data with real data through different component scores which evaluate the characteristics that are desirable for ``high quality'' synthetic data. Being a single score metric and having an implicit bound, TabSynDex can also be used to observe and evaluate the training of neural network based approaches. This would help in obtaining insights that was not possible earlier. We present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models. We also give a comparative analysis between TabSynDex and existing synthetic tabular data evaluation metrics. This shows the effectiveness and universality of our metric over the existing metrics. Source Code: \url{https://github.com/vikram2000b/tabsyndex}, Comment: IEEE Transactions on Artificial Intelligence

Published
2022

31. MINT Registry: Rationale and Study Design

Author

Kaustubh Limaye, Sami Al Kasab, Jaydevsinh Dolia, Mohamad Ezzeldin, Daniel Vela Duarte, Vinodh Doss, Sourabh Lahoti, David Hasan, Alejandro Spiotta, Khaled Asi, Vasu Saini, Tapan Mehta, Ameer Hassan, Diogo Haussen, Dileep Yavagal, Jesse Jones, Omar Tanweer, and Waleed Brinjikji

Subjects
Neurology. Diseases of the nervous system, RC346-429, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background Mechanical thrombectomy has become the standard of care for treatment of acute ischemic stroke secondary to large‐vessel occlusion up to 24 hours from last known normal time. Multiple different techniques for mechanical thrombectomy have been described, including a direct aspiration first‐pass technique and stent retriever thrombectomy. With a direct aspiration first‐pass technique, classically, a large‐bore aspiration catheter is delivered over a microcatheter and microwire to the clot. Recently, a novel macrowire has been introduced as a potential alternative to the use of microwire–microcatheter to allow the delivery of the aspiration catheter. The aim of this study is to develop a multicenter registry comparing delivery of an aspiration catheter for intracranial thrombectomy for acute ischemic stroke secondary to emergent large‐vessel occlusion over a macrowire in comparison with traditional use of microcatheter and microwire. Methods MINT (Macrowire for Intracranial Thrombectomy) is a multicenter, observational study currently enrolling patients with large‐vessel occlusion who underwent mechanical thrombectomy using a macrowire to deliver the aspiration catheter to the intracranial occlusion. All the participating sites will screen and report cases on a monthly basis. The decision to use the macrowire and various aspiration catheters is at the discretion of the interventionalist. Results We will collect patient's clinical, demographic, and radiographic data. In addition, we plan to collect procedure variables and postprocedure clinical and imaging data. Outcomes include successful delivery of the reperfusion catheter to the clot interface, time taken from groin access to first pass, and a bailout strategy for thrombectomy in cases where this is not feasible. Conclusion The MINT registry will add to our understanding of safety and efficacy of this novel macrowire in intracranial thrombectomy. This registry will also highlight and allow for understanding in workflow improvements from simplifying setup and possibly cost effectiveness of this technique.

Published
2024
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35. Preparation of Calcite-Precipitating Bacteria-Embedded Magnesium Phosphate Cement for Self-Healing Application

Author

Anupama Pable, Ashish Gadekar, Mukund Lahoti, Shreyas Pranav, Alankar Sapkal, Deeksha Patil, and Umesh Jadhav

Subjects
bacteria, calcite precipitation, magnesium phosphate cement, self-healing, Materials of engineering and construction. Mechanics of materials, TA401-492, Building construction, TH1-9745
Abstract

The present study was undertaken to check the feasibility of magnesium phosphate cement (MPC) for the immobilization of calcite-precipitating bacteria. An aqueous route of MPC synthesis was followed using magnesium phosphate Mg3(PO4)2 powder and ammonium phosphate solution. The Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) analysis confirmed the synthesis of MPC. The thermal decomposition analysis (TGA) showed decomposition of struvite between 50–60 °C - Paenibacillus sp. NCIM 5410 was used due to its urea hydrolysis ability. pH 9 was found to be optimum for urea hydrolysis. The urea hydrolysis steadily decreased with an increase in temperature from 30 °C to 60 °C. The hydrolysis was seen to increase with an incubation time of up to 72 h and subsequently reduced. The bacteria showed 90% urea hydrolysis at pH 9, 30 °C temperature, and after 72 h. The bacterial spores were incorporated during MPC synthesis, which helped their immobilization. The bacterial spore-containing MPC decomposed around 70 (±0.48)% of urea. Further, calcite precipitation was studied. The precipitate formed due to bacterial action in the MPC crack showed the presence of calcium. The calcite precipitation helped to reduce the water absorption by MPC specimens. The spore containing MPC specimens showed around 2.62 (±0.55) % water absorption. These results suggest that it is possible to synthesize bioactive MPC by immobilizing bacterial spores in MPC.

Published
2024
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40. Multi-class waste segregation using computer vision and robotic arm

Author

Jayanti Lahoti, Jathin Sn, M. Vamshi Krishna, Mallika Prasad, Rajeshwari BS, Namratha Mysore, and Jyothi S. Nayak

Subjects
Multi-class waste segreggation, Robotic arm, Computer vision, YOLO single shot detector, Electronic computers. Computer science, QA75.5-76.95
Abstract

Waste segregation is an essential aspect of a smoothly functioning waste management system. Usually, various recyclable waste types are disposed of together at the source, and this brings in the necessity to segregate them into their categories. Dry waste needs to be separated into its own categories to ensure that the proper procedures are implemented to treat and process it, which leads to an overall increased recycling rate and reduced landfill impact. Paper, plastics, metals, and glass are just a few examples of the many dry waste materials that can be recycled or recovered to create new goods or energy. Over the past years, much research has been conducted to devise effective and productive ways to achieve proper segregation for the waste that is being produced at an ever-increasing rate. This article introduces a multi-class garbage segregation system employing the YOLOv5 object detection model. Our final prototype demonstrates the capability of classifying dry waste categories and segregating them into their respective bins using a 3D-printed robotic arm. Within our controlled test environment, the system correctly segregated waste classes, mainly paper, plastic, metal, and glass, eight out of 10 times successfully. By integrating the principles of artificial intelligence and robotics, our approach simplifies and optimizes the traditional waste segregation process.

Published
2024
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41. Role of Instrument-assisted Soft Tissue Mobilisation in Text Neck Syndrome: A Quasi-experimental Study

Author

Amita Aggarwal, Anuj Pawan Lahoti, and Tushar J Palekar

Subjects
forward head posture, neck pain, repetitive stress, turtle neck posture, Medicine
Abstract

Introduction: Text Neck Syndrome occurs due to sustained Forward Head Posture (FHP) when smartphones are used constantly without any breaks for more than five hours and with improper posture. Long-term untreated text neck can cause inflammation of the neck’s ligaments, muscles, and nerves, leading to chronic arthritic changes. Literature provides studies evaluating the effects of physical or manual therapy, but there is a lack of studies investigating the effects of Instrument-assisted Soft Tissue Mobilisation (IASTM) treatment in individuals with text neck syndrome. Aim: To determine the added effect of IASTM on text neck syndrome. Materials and Methods: This quasi-experimental study was conducted at Department of Physiotherapy, Dr. D.Y. Patil College of Physiotherapy, Pune, Maharashtra, India, from August 2022 to December 2022. A total of 22 subjects, with a mean age of 21.58±1.18 years, participated in the study. The subjects were then divided into two treatment groups (n=11 each). The experimental group (Group-A) received stretching, strengthening exercises with IASTM, while the control group (Group-B) received only conventional physical therapy (stretching and strengthening). The treatment was delivered for a twoweek period. The data were analysed using MedCalc statistical software version 20.210. Significance was set at a p-value less than 0.05. For within-group analysis, Wilcoxon’s signed-rank or paired t-test was used depending on the normality distribution, and for between-group comparisons, Mann-Whitney tests were used for skewed variables. Results: The pain severity (p-value=0.0063) and neck disability (p-value=0.0008) were significantly lower in the IASTM group than the control group. Additionally, the Craniovertebral Angle (CVA) (p-value=0.0001) significantly increased with IASTM (Group-A) compared to the control group. Conclusion: The study concluded that IASTM and conventional therapy are individually effective in improving pain, disability, and FHP in text neck syndrome. However, adding IASTM to routine physical therapy showed statistically more significant results compared to physical therapy alone in reducing neck pain, disability, and FHP.

Published
2023
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46. Exploring the Factors Shaping Urban Greenspace Interactions: A Case Study of Nagpur, India

Author

Shruti Ashish Lahoti, Shalini Dhyani, and Osamu Saito

Subjects
Urban Green Space, urban sustainability, nature connection and urbanization, Agriculture
Abstract

This study aims to investigate how urban residents interact with Urban Green Spaces (UGSs) in Nagpur, India, specifically focusing on the patterns of visitation and engagement. Data were collected via a face-to-face questionnaire survey using the Survey 123 app. The analysis included interaction (types of UGSs, visitation frequency, and UGS availability); engagement (activities); demographics (age, gender, and work status); and nature connection (self-reported) aspects. Using data from 2002 participants, the study employs statistical analyses using R software (4.3.2) to explore the correlations between these variables. The results revealed key factors influencing UGS usage, highlighting the interplay between environmental and social aspects. Neighborhood UGSs, proximity, and accessibility were found to be pivotal in promoting frequent visitation, while physical activity emerged as the most common activity among daily visitors. Older adults visited UGSs less frequently, suggesting potential barriers, while employed individuals visited more often. A strong association between nature connection and UGS interaction was highlighted, emphasizing the psychological and emotional aspects of UGS usage. For example, individuals who felt more connected to nature reported using UGSs for physical activities, mental relaxation, and socializing. These findings underscore the need for integrating UGSs within broader urban social-ecological systems, which means recognizing these spaces as vital components contributing to overall health and resilience and catering to the population’s diverse needs, ensuring that these spaces are accessible and enjoyable for all community members, including those from different cultural, age, and socioeconomic backgrounds. Additionally, fostering nature connectedness through education and exposure to natural environments is recommended to enhance UGS usage, supporting broader urban planning strategies to create sustainable and healthy urban environments.

Published
2024
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