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10,027 results on '"Banga A"'

1. Conformal Prediction in Dynamic Biological Systems

Author

Portela, Alberto, Banga, Julio R., and Matabuena, Marcos

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Uncertainty quantification (UQ) is the process of systematically determining and characterizing the degree of confidence in computational model predictions. In the context of systems biology, especially with dynamic models, UQ is crucial because it addresses the challenges posed by nonlinearity and parameter sensitivity, allowing us to properly understand and extrapolate the behavior of complex biological systems. Here, we focus on dynamic models represented by deterministic nonlinear ordinary differential equations. Many current UQ approaches in this field rely on Bayesian statistical methods. While powerful, these methods often require strong prior specifications and make parametric assumptions that may not always hold in biological systems. Additionally, these methods face challenges in domains where sample sizes are limited, and statistical inference becomes constrained, with computational speed being a bottleneck in large models of biological systems. As an alternative, we propose the use of conformal inference methods, introducing two novel algorithms that, in some instances, offer non-asymptotic guarantees, enhancing robustness and scalability across various applications. We demonstrate the efficacy of our proposed algorithms through several scenarios, highlighting their advantages over traditional Bayesian approaches. The proposed methods show promising results for diverse biological data structures and scenarios, offering a general framework to quantify uncertainty for dynamic models of biological systems.The software for the methodology and the reproduction of the results is available at https://zenodo.org/doi/10.5281/zenodo.13644870.

Published
2024

2. Effects of Extracorporeal Membrane Oxygenation Initiation on Oxygenation and Pulmonary Opacities

Author

Batra Kiran, Mohanka Manish, Bollineni Srinivas, Kaza Vaidehi, Rajiah Prabhakar, Xi Yin, Hackmann Amy, Wait Michael, Torres Fernando, and Banga Amit

Subjects
ecmo, acute lung injury, cytokine surge, refractory hypoxia, systemic inflammatory response, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

There is limited data on the impact of extracorporeal membrane oxygenation (ECMO) on pulmonary physiology and imaging in adult patients. The current study sought to evaluate the serial changes in oxygenation and pulmonary opacities after ECMO initiation.

Published
2021
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3. PhishNet: A Phishing Website Detection Tool using XGBoost

Author

Kumar, Prashant, Antony, Kevin, Banga, Deepakmoney, and Sohal, Arshpreet

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

PhisNet is a cutting-edge web application designed to detect phishing websites using advanced machine learning. It aims to help individuals and organizations identify and prevent phishing attacks through a robust AI framework. PhisNet utilizes Python to apply various machine learning algorithms and feature extraction techniques for high accuracy and efficiency. The project starts by collecting and preprocessing a comprehensive dataset of URLs, comprising both phishing and legitimate sites. Key features such as URL length, special characters, and domain age are extracted to effectively train the model. Multiple machine learning algorithms, including logistic regression, decision trees, and neural networks, are evaluated to determine the best performance in phishing detection. The model is finely tuned to optimize metrics like accuracy, precision, recall, and the F1 score, ensuring reliable detection of both common and sophisticated phishing tactics. PhisNet's web application is developed using React.js, which allows for client-side rendering and smooth integration with backend services, creating a responsive and user-friendly interface. Users can input URLs and receive immediate predictions with confidence scores, thanks to a robust backend infrastructure that processes data and provides real-time results. The model is deployed using Google Colab and AWS EC2 for their computational power and scalability, ensuring the application remains accessible and functional under varying loads. In summary, PhisNet represents a significant advancement in cybersecurity, showcasing the effective use of machine learning and web development technologies to enhance user security. It empowers users to prevent phishing attacks and highlights AI's potential in transforming cybersecurity.

Published
2024

4. Generalized Inverse Optimal Control and its Application in Biology

Author

Banga, Julio R. and Sager, Sebastian

Subjects
Quantitative Biology - Quantitative Methods, Mathematics - Optimization and Control
Abstract

Living organisms exhibit remarkable adaptations across all scales, from molecules to ecosystems. We believe that many of these adaptations correspond to optimal solutions driven by evolution, training, and underlying physical and chemical laws and constraints. While some argue against such optimality principles due to their potential ambiguity, we propose generalized inverse optimal control to infer them directly from data. This novel approach incorporates multi-criteria optimality, nestedness of objective functions on different scales, the presence of active constraints, the possibility of switches of optimality principles during the observed time horizon, maximization of robustness, and minimization of time as important special cases, as well as uncertainties involved with the mathematical modeling of biological systems. This data-driven approach ensures that optimality principles are not merely theoretical constructs but are firmly rooted in experimental observations. Furthermore, the inferred principles can be used in forward optimal control to predict and manipulate biological systems, with possible applications in bio-medicine, biotechnology, and agriculture. As discussed and illustrated, the well-posed problem formulation and the inference are challenging and require a substantial interdisciplinary effort in the development of theory and robust numerical methods.

Published
2024

5. Parameter estimation in ODEs: assessing the potential of local and global solvers

Author

de Dios, M. Fernández, González-Rueda, Ángel M., Banga, Julio R., González-Díaz, Julio, and Penas, David R.

Subjects
Mathematics - Optimization and Control, Quantitative Biology - Quantitative Methods, 90C26
Abstract

We consider the problem of parameter estimation in dynamic systems described by ordinary differential equations. A review of the existing literature emphasizes the need for deterministic global optimization methods due to the nonconvex nature of these problems. Recent works have focused on expanding the capabilities of specialized deterministic global optimization algorithms to handle more complex problems. Despite advancements, current deterministic methods are limited to problems with a maximum of around five state and five decision variables, prompting ongoing efforts to enhance their applicability to practical problems. Our study seeks to assess the effectiveness of state-of-the-art general-purpose global and local solvers in handling realistic-sized problems efficiently, and evaluating their capabilities to cope with the nonconvex nature of the underlying estimation problems.

Published
2024

6. Cause and Effect: Can Large Language Models Truly Understand Causality?

Author

Ashwani, Swagata, Hegde, Kshiteesh, Mannuru, Nishith Reddy, Jindal, Mayank, Sengar, Dushyant Singh, Kathala, Krishna Chaitanya Rao, Banga, Dishant, Jain, Vinija, and Chadha, Aman

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

With the rise of Large Language Models(LLMs), it has become crucial to understand their capabilities and limitations in deciphering and explaining the complex web of causal relationships that language entails. Current methods use either explicit or implicit causal reasoning, yet there is a strong need for a unified approach combining both to tackle a wide array of causal relationships more effectively. This research proposes a novel architecture called Context Aware Reasoning Enhancement with Counterfactual Analysis(CARE CA) framework to enhance causal reasoning and explainability. The proposed framework incorporates an explicit causal detection module with ConceptNet and counterfactual statements, as well as implicit causal detection through LLMs. Our framework goes one step further with a layer of counterfactual explanations to accentuate LLMs understanding of causality. The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning. The counterfactual sentences add explicit knowledge of the not caused by scenarios. By combining these powerful modules, our model aims to provide a deeper understanding of causal relationships, enabling enhanced interpretability. Evaluation of benchmark datasets shows improved performance across all metrics, such as accuracy, precision, recall, and F1 scores. We also introduce CausalNet, a new dataset accompanied by our code, to facilitate further research in this domain.

Published
2024

7. Design and Evaluation of a Socially Assistive Robot Schoolwork Companion for College Students with ADHD

Author

O'Connell, Amy, Banga, Ashveen, Ayissi, Jennifer, Yaminrafie, Nikki, Ko, Ellen, Le, Andrew, Cislowski, Bailey, and Matarić, Maja

Subjects
Computer Science - Robotics
Abstract

College students with ADHD respond positively to simple socially assistive robots (SARs) that monitor attention and provide non-verbal feedback, but studies have been done only in brief in-lab sessions. We present an initial design and evaluation of an in-dorm SAR study companion for college students with ADHD. This work represents the introductory stages of an ongoing user-centered, participatory design process. In a three-week within-subjects user study, university students (N=11) with self-reported symptoms of adult ADHD had a SAR study companion in their dorm room for two weeks and a computer-based system for one week. Toward developing SARs for long-term, in-dorm use, we focus on 1) evaluating the usability and desire for SAR study companions by college students with ADHD and 2) collecting participant feedback about the SAR design and functionality. Participants responded positively to the robot; after one week of regular use, 91% (10 of 11) chose to continue using the robot voluntarily in the second week.

Published
2024
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8. 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

9. Getting a Load of Linguistic Reasoning: How L1 Student Teachers Process Rules of Thumb and Linguistic Manipulations in Discussions about Grammar

Author

Jimmy Van Rijt, Arina Banga, and Martijn Goudbeek

Abstract

An important skill for L1 language teachers when teaching grammar is the ability to produce and quickly evaluate arguments that underpin a grammatical analysis. Previous research has revealed that the strongest arguments in favour of a particular grammatical analysis are based on linguistic manipulations (LM) rather than on rules of thumb (RoT). This makes it critical for teachers to be able to handle arguments based on LM. If LM are considered too difficult to process compared to RoT, however, (student) teachers may avoid grammatical argumentation based on LM altogether, and they might struggle to evaluate their pupils' LM-based grammatical argumentation. The current study has therefore examined whether LM impose a higher cognitive load on Dutch student teachers than RoT, using grammatical discussion tasks in which participants (N = 298) evaluated arguments based on RoT and on LM. Multilevel analyses indicate that LM are indeed more difficult to process than RoT, as measured by response times, correct classifications, and perceived difficulty ratings. This effect is partly influenced by student teachers' need for cognition and their willingness to engage in grammar.

Published
2024
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10. Theorising Racist Hate Speech on UK University Campuses through a CRT Lens

Author

Sunil Banga

Abstract

Racist hate speech on UK university campuses remains an increasing concern. This article examines the widespread problem of racist hate speech through a multilayered theoretical approach; and suggests that a fuller appreciation of the problem can only be gained through a race-centred analysis. Drawing on Fraser's work on the problematics of misrecognition and identity-reification, the article extends Levine-Rasky's theoreticisation of power dynamics in the intersectionality between whiteness and middle-classness to intersections of whiteness with both middle-classness and working-classness as a contributing factor to mobilisation of lad cultures which is termed as white-laddism. The article advances the understanding of racist hate speech through a conjoined analysis of these concepts through a Critical Race Theory (CRT) lens. The conjoined analysis is informed by the Deleuzian 'rhizomatic' approach in order to examine the process by which multiple, diverse and non-hierarchical lines of connection between the concepts shape, dislocate, modify each other in variable ways. The detail behind the bigger picture helps further our understanding of the inadequate nature of equality policies and strategies that often look at issues in isolation, and thus fail to address the inequality and injustice being perpetuated on campuses.

Published
2024
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16. Transdermal bisoprolol for prevention of postoperative atrial fibrillation: A systematic review and meta‐analysis

Author

Andrew G. Kim, Sandeep Banga, Qi Xuan Ang, Lalitsiri Atti, Harith Ghnaima, Saif AlAttal, Preeti Banga, and Matthew D. Wilcox

Subjects
Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background The transdermal patch of bisoprolol available in Japan has been reported to demonstrate superior efficacy in preventing postoperative atrial fibrillation, possibly surpassing its oral counterpart. However, there has been no systematic review and meta‐analysis assessing the efficacy of transdermal bisoprolol. Methods A comprehensive systematic literature search was conducted on PubMed, Embase, and Cochrane to identify all relevant studies assessing the efficacy of transdermal bisoprolol in preventing postoperative atrial fibrillation. The search covered studies from inception up to December 4, 2023. For data analysis, Review Manager (RevMan) 5.4 software was employed, using a random‐effects model to calculate risk ratios (RR) and 95% confidence intervals (CI). Results Three studies, comprising a total of 551 patients (transdermal bisoprolol 228 and control 323), were included. There was a decreased risk of postoperative atrial fibrillation or atrial tachyarrhythmias in patients treated with transdermal bisoprolol (RR 0.43, 95% CI 0.27–0.67, p = .0002, I2 = 0%). Conclusion Transdermal administration of bisoprolol has consistently shown efficacy, and this pooled analysis supports its effectiveness. The heterogeneity of the included studies limits certain interpretations. Future randomized clinical trials may elucidate the superiority of transdermal administration over oral administration.

Published
2024
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18. Resource Aware Clustering for Tackling the Heterogeneity of Participants in Federated Learning

Author

Mishra, Rahul, Gupta, Hari Prabhat, and Banga, Garvit

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Federated Learning is a training framework that enables multiple participants to collaboratively train a shared model while preserving data privacy and minimizing communication overhead. The heterogeneity of devices and networking resources of the participants delay the training and aggregation in federated learning. This paper proposes a federated learning approach to manoeuvre the heterogeneity among the participants using resource aware clustering. The approach begins with the server gathering information about the devices and networking resources of participants, after which resource aware clustering is performed to determine the optimal number of clusters using Dunn Indices. The mechanism of participant assignment is then introduced, and the expression of communication rounds required for model convergence in each cluster is mathematically derived. Furthermore, a master-slave technique is introduced to improve the performance of the lightweight models in the clusters using knowledge distillation. Finally, experimental evaluations are conducted to verify the feasibility and effectiveness of the approach and to compare it with state-of-the-art techniques., Comment: 13 pages, 4 figures

Published
2023

28. CULTURAL CAPITAL IN BMD: TRACING A PRE-BUILT DISCOURSE

Author

Jean Désiré BANGA AMVÉNÉ

Subjects
capital, credit, bmd, pre-construct bologna process., Social sciences (General), H1-99, Education, Psychology, BF1-990
Abstract

The university reform known as the BMD (Bachelor-Master-Doctorate) system has turned science into capital with cumulative and transferable credits. This paper is informed by the theoretical perspective of Bakhtin’s dialogism and views this as a pre-built, an earlier word taken up in discourses as self-evident, even though its source is unknown. Where is this pre-built from? This article argues that the capitalist pre-built originates in certain instances of global power. To reveal its path and understand its motivations, the article analyses a group of texts including a pedagogical regulation and the Declarations of Libreville and Bologna.

Published
2024
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29. Understanding Language Teacher Wellbeing: An ESM Study of Daily Stressors and Uplifts

Author

Gregersen, Tammy, Mercer, Sarah, MacIntyre, Peter, Talbot, Kyle, and Banga, Claire Ann

Abstract

This study focuses on understanding language teachers' lived experiences of their stressors and positive uplifts from a holistic perspective covering their professional lives in school, their personal lives beyond, and the connection between the two. The aim was to explore the nature of teachers' experiences of stress and how they spilled over from work into home domains. We also were keen to understand the dynamics of their experiences of stress and how their perception of daily stressors was related to their overall sense of wellbeing as well as their life and chronic stressors. The data were collected via a specially created app, which collected survey data and experience sampling method (ESM) data from language teachers across the globe. Teachers' wellbeing was investigated using the PERMA Profiler (Butler & Kern, 2016), their personality using Goldberg's (1992) Big Five measurement tool, and a questionnaire on chronic stressors and stressful life events. From a larger sample (n = 47), a set of 6 case studies of teachers who scored highly for wellbeing and those who scored low on wellbeing was examined to explore in depth and across time, the relationships between overall wellbeing, chronic stressors and stressful life events, the experience of daily stressors, and perceptions of health. The findings point to the complexity of the relationships between stress, wellbeing, and health as well as the dynamism of stress and the relationships between stress experienced in the workplace and at home. The study has important implications for research in this area and reveals the merits of working with this innovative data collection tool.

Published
2023
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33. The recommendations and the way forward to fight human infections caused by influenza A (H1N1) in Brazil

Author

Styves Banga, Fabien Balagizi, Ibad ur Rehman, Malik Olatunde Oduoye, Rodrigue Fikiri Bavurhe, Hugues Cakwira, Chrispin Biamba, Gaston Masimango, Hardy Elembwe, and Aymar Akilimali

Subjects
Animals, H1N1 subtype, Humans, Influenza A virus, Influenza human diagnosis, Infectious and parasitic diseases, RC109-216
Abstract

Many recent outbreaks of influenza A (H1N1) in the world, especially in Brazil, it has become clear that the severity of the disease is not known in the same form. On Wednesday, June 7, 2023, Brazil notified the WHO of a fatal case of human infection with a variant of the influenza A(H1N1) virus of swine origin, this case was confirmed in a laboratory in the region of the interior state of Paraná. This is the first human infection caused by an influenza A (H1N1) virus reported in 2023 nationwide in Brazil. To mitigate H1N1 flu in Brazil, we urge the Brazillian government through its Ministry of Health to improve on mass awareness about the signs and symptoms of H1N1 flu among the Brazillians. The Brazillian government should also implement the One Health approach towards the control of H1N1 flu in Brazil, as we believe that these recommendations would go a long way in preventing future cases and the spread of H1N1 flu in Brazil. This article aims to present the clinical presentations of the H1N1 flu and the implications, recommendations and the way forward to protect the Brazilian population against the H1N1 flu.

Published
2024
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35. فاعلية برنامج تدريبي في تعزيز الصحة النفسية للأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك

Author

Dr. Abdul Hamid Hassan Haj Amin and Professor Mansour Banga Hajar Muhammad

Subjects
التكيف، التكامل النفسي، تحقيق الذات, Social Sciences
Abstract

هدفت الدراسة إلى التعرف على فاعلية برنامج تدريبي في تعزيز الصحة النفسية لدي الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك, ومعرفة الفروق ذات الدلالة الإحصائية بين إجابات الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك، قبل تطبيق البرنامج التدريبي وبعد تطبيقه, ومعرفة الفروق ذات الدلالة الإحصائية بين إجابات الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك ترجع لمتغيرات الدارسة النوع, نوع الكرسي, تصنيف الإعاقة, استخدمت الدراسة المنهج شبه التجريبي, والبرنامج التدريبي والمقياس أدوات لها تمثل مجتمع الدراسة في الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك جامعة الجزيرة، واتخذت الدراسة عينة قصدية تمثلت في جميع الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك والبالغ عددهم (13) شخص, وتم تحليل النتائج بواسطة برنامج الحزم الإحصائية للعلوم الاجتماع (spss), وتوصلت الدراسة إلي أنه توجد فروق ذات دلالة إحصائية بين إجابات الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك قبل تطبيق البرنامج التدريبي وبعد تطبيقه, لصالح إجابات الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك بعد تطبيق البرنامج, و لا توجد فروق ذات دلالة إحصائية بين إجابات الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك, بعد تطبق البرنامج ترجع لنوع الشخص, و أنه لا توجد فروق ذات دلالة إحصائية بين إجابات الأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك ترجع لنوع الكرسي, لصالح الأشخاص مستخدمي الكرسي الآلي, و توصي الدراسة بتكاتف الجهود لتعزيز الصحة النفسية للأشخاص ذوي الإعاقة الجسدية مستخدمي الكرسي المتحرك.

Published
2024
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36. Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers.

Author

David R Penas, Meysam Hashemi, Viktor K Jirsa, and Julio R Banga

Subjects
Biology (General), QH301-705.5
Abstract

The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic algorithm capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking on synthetic data, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added a scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process.

Published
2024
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37. Background incidence rates of health outcomes of interest for COVID-19 vaccine safety monitoring in a US population: a claims database analysis

Author

Sonja Banga, Alena Khromava, Laurence Serradell, Anne-Laure Chabanon, Chunshen Pan, Irisdaly Estevez, Samantha Schilsky, and Hannah Kreisberg

Subjects
Medicine
Abstract

Objective To evaluate background incidence rates of 59 health outcomes of interest (HOI) in a diverse population, including important subpopulations, during the pre-COVID-19 era (1 January 2017–31 December 2019) and the COVID-19 era (1 March 2020–31 December 2020), before the introduction of COVID-19 vaccines.Design Observational retrospective cohort study. Annual incidence rates and 95% confidence intervals (CIs) of HOIs were estimated for each population of interest, stratified by: age, sex, age and sex and seasonality.Data source Optum’s de-identified Clinformatics Data Mart Database (CDM).Participants Individuals from the US general population and four subgroups of interest: influenza-vaccinated, paediatric (

Published
2024
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38. Symbiotic Narratives for a De-Colonial Turn

Author

Banga, Harjot

Subjects
Arboreal symbiosis. Decolonial arboreal identities. Human-tree dynamics. Postcolonial narratives. Roy’s botanical discourse, Environmental sciences, GE1-350, Anthropology, GN1-890, Arts in general, NX1-820
Abstract

This study delves into the nuanced relationship between humans and trees, focusing on Sumana Roy’s Out of Syllabus (2019). Using a multidisciplinary approach drawing from anthropology and philosophy, it aims to uncover the dynamic role of trees in the human experience in India. Positioned within a postcolonial perspective aligned with the decolonial turn, the analysis emphasises trees’ active agency, challenging their traditional portrayal as passive elements. Informed by scholars like Jones and Oppermann, it delves into the nuanced interplay between human and arboreal existence, enhancing our understanding of their symbiotic relationship.

Published
2024
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39. Developmental consequences of the digitally-mediated landscape : implications for empathic processing at the neural and behavioural levels-of-analysis during the early adolescent pubertal transition

Author

Banga, Claire Ann, Schwannauer, Matthias, Quayle, Ethel, Auyeung, Bonnie, and Gherri, Elena

Subjects
digital technology, Empathy, adolescence, smartphone, experience sampling, ecological momentary assessment, event related potentials, screen-time
Abstract

BACKGROUND: Empathy is fundamental to the adaptive development of emotional and prosocial functioning in childhood and adolescence (Eisenberg, 2018); whereas, a number of forms of psychopathology and maladaptive developmental outcomes are associated with marked deficits in empathic processing (Farrell & Vaillancourt, 2021). Cross temporal meta-analytic evidence indicates a concerning trend of average levels of empathy decreasing in younger generational cohorts (Konrath et al., 2011), while concurrently, mental health difficulties such as depression, anxiety, suicidality, and loneliness are more prevalent in younger generations (Twenge et al., 2018). These generational patterns map onto the historical invention, subsequent adoption, and eventual market saturation of digital technologies including the internet, smartphones, and social media. Such trends have given rise to the rewiring hypothesis which contends that the substantial amounts of time spent engaged with digital technologies may be resulting in neuroplastic changes in developing brains that may be impeding adaptive emotional functioning (Greenfield, 2014; Montag & Diefenbach, 2018). AIMS: The current project addresses the primary research question (RQ) of whether the vast range of digital technology-based activities that young people engage in on a daily basis - termed 'screen-time' (ST) - may be impacting their empathic development. The theoretical framework of the Perception-Action Model of Empathy (Preston & de Waal, 2002) was employed as the basis for operationalizing the study of empathy. The first set of RQs ask whether higher levels of ST predict deficits in cognitive (RQ1.1) and affective (RQ1.2) empathy. The second set of RQs evaluate the predictive validity of subjective versus objective measures of ST (RQ2.1) and the statistical convergence between behavioural, neurophysiological, and global-report measures of empathy (RQ2.2). SAMPLE: The pubertal transition is a particularly important period in neural development as substantive changes in neurobiological organization occur that create a vulnerability to maladaptive affective outcomes (Guyer et al., 2016). Thus, the current study drew from a sample of typically developing preadolescent children (11 to 14 years of age) which marks an age group both beginning and in the midst of puberty. The project consisted of a design of three successive studies which drew from the same sample recruited from Scottish secondary schools. METHOD: Study 1 (n = 263) consisted of a smartphone-based study which combined ecological momentary assessment (EMA) with the collection of passive smartphone sensing data including objective time estimates of smartphone screen-time (SMT). The study used eMoodie (emoodie.com), an application specifically designed for developmental research, which was developed and validated in the context of this project. The design allowed for the momentary assessment of ST by young people, in their everyday environments, in a way that could help to operationally define and measure increasingly complex patterns of use such as media multitasking (MMT). Using multi-level modelling, comparisons between EMA surveys, a variety of global measures of SMT, MMT, and smartphone addiction, were made relative to objective data on sensed SMT. It was hypothesized that subjective measures would show poor convergent validity with objective data (H2.1). Study 2 (n = 165) was a behavioural study of empathy which employed a video-based empathic accuracy task as a performance-based measure of cognitive empathy (i.e., perspective taking; Zaki & Ochsner, 2011) with a stimulus set which was also developed and validated in the context of this project. It was hypothesized that empathic accuracy performance would be inversely related to ST (H1.1), and it would show poor convergence with child self-reported empathy scores (H2.2). Study 3 (n = 39) was a laboratory-based neurophysiological study of pain empathy using event-related potentials (ERPs) based on a well-validated task which is thought to directly measure affective empathy based on the temporal resolution of the processes being measured (Coll, 2018; Fan & Han, 2008). Importantly, participants were grouped based on high versus low ST levels to compare differences in empathic processing at the neural level-of-analysis. It was hypothesized that there would be deficits in pain empathic processing in the high but not the low ST group (H1.2). Finally, by combining the results across the three studies, statistical models evaluating the relationship between ST and empathy were conducted, whilst controlling for the child's sex and parental attachment style. RESULTS: Findings from Study 1 established the feasibility and acceptability of the use of eMoodie as a research tool with high overall compliance (85%) in the weeklong EMA study (Domoff, Banga, et al., 2021). EMA measures of SMT were the best predictor of sensed SMT, while global estimates bore no statistical relationship to actual time usage (H2.1). Further analyses sought to examine group differences in SMT based on self-reported smartphone addiction which detected a bias in reporting whereby those with normative usage overreported time spent, whereas those with problematic usage underreported SST. The nature of these systematic errors would theoretically lead to a pattern of results in the evidence-base which fail to detect a true effect (i.e., Type II error) which may help to explain inconsistent results in media-effects research (e.g., Ivie et al., 2020). Results of Study 2 and 3 which compare the statistical convergence across the multiple methodologies employed to study empathy in this project, indicated that empathic accuracy task scores and ERP measures tapped different sources of variance. This supports a multidimensional model of empathy comprising both cognitive and affective dimensions. When compared to global questionnaires, no child self-report empathy measures yielded any statistical relationship with these more objective methodologies (H2.1-2.2), with the only predictive variable being parental reports of the child's perspective-taking abilities. In Study 3, ERP measures showed group differences based on ST consumption in both early and late components of pain empathy whereby the high ST group showed deficits in empathic processing. Therefore, the current findings provide neurophysiological evidence of the rewiring hypothesis (H1.2). Statistical modelling showed that ST predicted empathy across both cognitive and affective dimensions after controlling for important covariates (H1.1-1.2). CONCLUSIONS: Taken together, this project's findings establish a detrimental effect of ST on the development of empathy both at the behavioural and neural levels-of-analysis in a preadolescent sample. The implications are that high levels of ST may lead to deficits in social skills facilitated by empathy such as prosociality. From a developmental psychopathology perspective, high levels of ST may increase the probability of maladaptive developmental trajectories linked to psychopathology. Moreover, the results of the current project indicate substantial measurement error and poor validity in the dominant single time-point self-report methods used in media-effects and psychological research on empathy.

Published
2023
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48. Non-invasive ventilation for acute exacerbation of COPD with very high PaCO 2 : A randomized controlled trial

Author

Khilnani Gopi, Saikia Nripen, Banga Amit, and Sharma Surendra

Subjects
COPD exacerbation, non-invasive ventilation, randomized controlled trial, Diseases of the respiratory system, RC705-779
Abstract

Objective: To assess the role of non-invasive positive pressure ventilation (NIPPV) for management of Indian patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Materials and Methods: Forty patients (mean age 57.6 ± 10.8 years; M:F 31:9) with AECOPD with pH Results: Mean pH at baseline for N and C groups were similar (7.23 ± 0.07) whereas PaCO 2 was 85.4 ± 14.8 and 81.1 ± 11.6 mm of Hg, respectively. At one hour, patients in N group had greater improvement in pH (P = 0.017) as well as PaCO 2 (P = 0.04) which corroborated with clinical improvement. Whereas need of ETI was reduced in patients who received NIPPV (3/20 vs 12/20, P = 0.003), in-hospital mortality was similar (3/20 and 2/20, P = NS). The mean duration of hospital stay was significantly shorter in N group (9.4 ± 4.3 days) as compared to C group (17.8 ± 2.6 days); P = 0.001. Conclusions: In patients with AECOPD, NIPPV leads to rapid improvement in blood gas parameters and reduces the need for ETI

Published
2010

49. Pour une armée nationale, républicaine et intégrée en République Démocratique du Congo

Author

Elie NTUMBA MULAMBA, Guelord KUKETUKA MBAKATA, and Jean-Paul SOROBA BANGA

Subjects
armée, nation, république, intégration, Sociology (General), HM401-1281
Abstract

Depuis 1960, année de son accession à la souveraineté nationale et internationale, la République Démocratique du Congo, deuxième plus vaste pays d’Afrique disposant d’incommensurables ressources naturelles, demeure, jusqu’à ce jour, le théâtre des événements les plus tragiques et connaissant, sans répit, des sécessions, des rébellions, des assassinats, des guerres d’agression, des pillages sauvages des ressources naturelles, des viols massifs des femmes, des massacres. Pour nous, l’une des façons pour mettre fin à cette spirale de violences, reste sans conteste, la formation d’une armée forte, républicaine et dissuasive.

Published
2024

50. Distolingual root prevalence in mandibular first molar and complex root canal morphology in incisors: a CBCT analysis in Indian population

Author

Komal Sheth, K. S. Banga, Ajinkya Pawar, Dian Agustin Wahjuningrum, and Mohmed Isaqali Karobari

Subjects
Medicine, Science
Abstract

Abstract Cone-beam computed tomography was used to understand the possible correlation between the prevalence of distolingual root (DLR) in permanent mandibular first molars (MFMs) and the associated complicated mandibular incisor’s root canal morphology (MIs) in an Indian population. A total of 400 scans were evaluated for MFMs and MIs. The prevalence of DLRs and root canal anatomy of MIs were assessed based on Vertucci’s classification, and then the sample were grouped according to age, sex and side. Statistical analysis was used to evaluate the possible correlation between the presence of DLRs in the first molar and root canal morphology of incisors. Chi square test was used to evaluate the correlation between the root canal configurations of MIs with the existence of DLRs in MFMs. There was no statistically significant difference between sexes or ages for the prevalence of DLRs in the first molars (p > 0.05), which was 6.62%, with the right side having a greater frequency of DLRs (7.8%) than the left (5.5%). Vertucci Type I canal configuration was most common for the mandibular central (66.75%) and lateral incisors (58.62%). Vertucci Type III was the most common complicated canal morphology, followed by Types V, II, and IV for MIs, with no statistically significant difference in the studied sample's age and sex. (p

Published
2024
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