Bone Plug in the Bronchoscopic Management of Postoperative Bronchopleural Fistulas

Autor: Duy Kevin Duong, DO, Labib Gilles Debiane, MD, Avi Cohen, MD, A. Rolando Peralta, MD, Javier Diaz-Mendoza, MD, Michael J. Simoff, MD

Publikováno v: Annals of Thoracic Surgery Short Reports, Vol 2, Iss 3, Pp 427-431 (2024)

Background: Bronchopleural fistula (BPF) is a rare and often difficult postoperative complication to manage. This case series describes a bronchoscopic technique using a bone plug for closure of BPFs. Methods: Six patients at Henry Ford Hospital from

Externí odkaz: https://doaj.org/article/fd167fc78bc64638a2a66a5256b5d941

Bayesian algorithmic perfumery: A Hierarchical Relevance Vector Machine for the Estimation of Personalized Fragrance Preferences based on Three Sensory Layers and Jungian Personality Archetypes

Autor: Martinez, Rolando Gonzales

This study explores a Bayesian algorithmic approach to personalized fragrance recommendation by integrating hierarchical Relevance Vector Machines (RVM) and Jungian personality archetypes. The paper proposes a structured model that links individual s

Externí odkaz: http://arxiv.org/abs/2411.03965

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

Autor: da Silva, Rafael Ferreira, Bard, Deborah, Chard, Kyle, de Witt, Shaun, Foster, Ian T., Gibbs, Tom, Goble, Carole, Godoy, William, Gustafsson, Johan, Haus, Utz-Uwe, Hudson, Stephen, Jha, Shantenu, Los, Laila, Paine, Drew, Suter, Frédéric, Ward, Logan, Wilkinson, Sean, Amaris, Marcos, Babuji, Yadu, Bader, Jonathan, Balin, Riccardo, Balouek, Daniel, Beecroft, Sarah, Belhajjame, Khalid, Bhattarai, Rajat, Brewer, Wes, Brunk, Paul, Caino-Lores, Silvina, Casanova, Henri, Cassol, Daniela, Coleman, Jared, Coleman, Taina, Colonnelli, Iacopo, Da Silva, Anderson Andrei, de Oliveira, Daniel, Elahi, Pascal, Elfaramawy, Nour, Elwasif, Wael, Etz, Brian, Fahringer, Thomas, Ferreira, Wesley, Filgueira, Rosa, Tande, Jacob Fosso, Gadelha, Luiz, Gallo, Andy, Garijo, Daniel, Georgiou, Yiannis, Gritsch, Philipp, Grubel, Patricia, Gueroudji, Amal, Guilloteau, Quentin, Hamalainen, Carlo, Enriquez, Rolando Hong, Huet, Lauren, Kesling, Kevin Hunter, Iborra, Paula, Jahangiri, Shiva, Janssen, Jan, Jordan, Joe, Kanwal, Sehrish, Kunstmann, Liliane, Lehmann, Fabian, Leser, Ulf, Li, Chen, Liu, Peini, Luettgau, Jakob, Lupat, Richard, Fernandez, Jose M., Maheshwari, Ketan, Malik, Tanu, Marquez, Jack, Matsuda, Motohiko, Medic, Doriana, Mohammadi, Somayeh, Mulone, Alberto, Navarro, John-Luke, Ng, Kin Wai, Noelp, Klaus, Kinoshita, Bruno P., Prout, Ryan, Crusoe, Michael R., Ristov, Sashko, Robila, Stefan, Rosendo, Daniel, Rowell, Billy, Rybicki, Jedrzej, Sanchez, Hector, Saurabh, Nishant, Saurav, Sumit Kumar, Scogland, Tom, Senanayake, Dinindu, Shin, Woong, Sirvent, Raul, Skluzacek, Tyler, Sly-Delgado, Barry, Soiland-Reyes, Stian, Souza, Abel, Souza, Renan, Talia, Domenico, Tallent, Nathan, Thamsen, Lauritz, Titov, Mikhail, Tovar, Benjamin, Vahi, Karan, Vardar-Irrgang, Eric, Vartina, Edite, Wang, Yuandou, Wouters, Merridee, Yu, Qi, Bkhetan, Ziad Al, Zulfiqar, Mahnoor

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogen

Externí odkaz: http://arxiv.org/abs/2410.14943

Quantum error correction below the surface code threshold

Autor: Acharya, Rajeev, Aghababaie-Beni, Laleh, Aleiner, Igor, Andersen, Trond I., Ansmann, Markus, Arute, Frank, Arya, Kunal, Asfaw, Abraham, Astrakhantsev, Nikita, Atalaya, Juan, Babbush, Ryan, Bacon, Dave, Ballard, Brian, Bardin, Joseph C., Bausch, Johannes, Bengtsson, Andreas, Bilmes, Alexander, Blackwell, Sam, Boixo, Sergio, Bortoli, Gina, Bourassa, Alexandre, Bovaird, Jenna, Brill, Leon, Broughton, Michael, Browne, David A., Buchea, Brett, Buckley, Bob B., Buell, David A., Burger, Tim, Burkett, Brian, Bushnell, Nicholas, Cabrera, Anthony, Campero, Juan, Chang, Hung-Shen, Chen, Yu, Chen, Zijun, Chiaro, Ben, Chik, Desmond, Chou, Charina, Claes, Jahan, Cleland, Agnetta Y., Cogan, Josh, Collins, Roberto, Conner, Paul, Courtney, William, Crook, Alexander L., Curtin, Ben, Das, Sayan, Davies, Alex, De Lorenzo, Laura, Debroy, Dripto M., Demura, Sean, Devoret, Michel, Di Paolo, Agustin, Donohoe, Paul, Drozdov, Ilya, Dunsworth, Andrew, Earle, Clint, Edlich, Thomas, Eickbusch, Alec, Elbag, Aviv Moshe, Elzouka, Mahmoud, Erickson, Catherine, Faoro, Lara, Farhi, Edward, Ferreira, Vinicius S., Burgos, Leslie Flores, Forati, Ebrahim, Fowler, Austin G., Foxen, Brooks, Ganjam, Suhas, Garcia, Gonzalo, Gasca, Robert, Genois, Élie, Giang, William, Gidney, Craig, Gilboa, Dar, Gosula, Raja, Dau, Alejandro Grajales, Graumann, Dietrich, Greene, Alex, Gross, Jonathan A., Habegger, Steve, Hall, John, Hamilton, Michael C., Hansen, Monica, Harrigan, Matthew P., Harrington, Sean D., Heras, Francisco J. H., Heslin, Stephen, Heu, Paula, Higgott, Oscar, Hill, Gordon, Hilton, Jeremy, Holland, George, Hong, Sabrina, Huang, Hsin-Yuan, Huff, Ashley, Huggins, William J., Ioffe, Lev B., Isakov, Sergei V., Iveland, Justin, Jeffrey, Evan, Jiang, Zhang, Jones, Cody, Jordan, Stephen, Joshi, Chaitali, Juhas, Pavol, Kafri, Dvir, Kang, Hui, Karamlou, Amir H., Kechedzhi, Kostyantyn, Kelly, Julian, Khaire, Trupti, Khattar, Tanuj, Khezri, Mostafa, Kim, Seon, Klimov, Paul V., Klots, Andrey R., Kobrin, Bryce, Kohli, Pushmeet, Korotkov, Alexander N., Kostritsa, Fedor, Kothari, Robin, Kozlovskii, Borislav, Kreikebaum, John Mark, Kurilovich, Vladislav D., Lacroix, Nathan, Landhuis, David, Lange-Dei, Tiano, Langley, Brandon W., Laptev, Pavel, Lau, Kim-Ming, Guevel, Loïck Le, Ledford, Justin, Lee, Kenny, Lensky, Yuri D., Leon, Shannon, Lester, Brian J., Li, Wing Yan, Li, Yin, Lill, Alexander T., Liu, Wayne, Livingston, William P., Locharla, Aditya, Lucero, Erik, Lundahl, Daniel, Lunt, Aaron, Madhuk, Sid, Malone, Fionn D., Maloney, Ashley, Mandrá, Salvatore, Martin, Leigh S., Martin, Steven, Martin, Orion, Maxfield, Cameron, McClean, Jarrod R., McEwen, Matt, Meeks, Seneca, Megrant, Anthony, Mi, Xiao, Miao, Kevin C., Mieszala, Amanda, Molavi, Reza, Molina, Sebastian, Montazeri, Shirin, Morvan, Alexis, Movassagh, Ramis, Mruczkiewicz, Wojciech, Naaman, Ofer, Neeley, Matthew, Neill, Charles, Nersisyan, Ani, Neven, Hartmut, Newman, Michael, Ng, Jiun How, Nguyen, Anthony, Nguyen, Murray, Ni, Chia-Hung, O'Brien, Thomas E., Oliver, William D., Opremcak, Alex, Ottosson, Kristoffer, Petukhov, Andre, Pizzuto, Alex, Platt, John, Potter, Rebecca, Pritchard, Orion, Pryadko, Leonid P., Quintana, Chris, Ramachandran, Ganesh, Reagor, Matthew J., Rhodes, David M., Roberts, Gabrielle, Rosenberg, Eliott, Rosenfeld, Emma, Roushan, Pedram, Rubin, Nicholas C., Saei, Negar, Sank, Daniel, Sankaragomathi, Kannan, Satzinger, Kevin J., Schurkus, Henry F., Schuster, Christopher, Senior, Andrew W., Shearn, Michael J., Shorter, Aaron, Shutty, Noah, Shvarts, Vladimir, Singh, Shraddha, Sivak, Volodymyr, Skruzny, Jindra, Small, Spencer, Smelyanskiy, Vadim, Smith, W. Clarke, Somma, Rolando D., Springer, Sofia, Sterling, George, Strain, Doug, Suchard, Jordan, Szasz, Aaron, Sztein, Alex, Thor, Douglas, Torres, Alfredo, Torunbalci, M. Mert, Vaishnav, Abeer, Vargas, Justin, Vdovichev, Sergey, Vidal, Guifre, Villalonga, Benjamin, Heidweiller, Catherine Vollgraff, Waltman, Steven, Wang, Shannon X., Ware, Brayden, Weber, Kate, White, Theodore, Wong, Kristi, Woo, Bryan W. K., Xing, Cheng, Yao, Z. Jamie, Yeh, Ping, Ying, Bicheng, Yoo, Juhwan, Yosri, Noureldin, Young, Grayson, Zalcman, Adam, Zhang, Yaxing, Zhu, Ningfeng, Zobrist, Nicholas

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential sup

Externí odkaz: http://arxiv.org/abs/2408.13687

Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle

Autor: Garcia, Rolando, Kallanagoudar, Pragya, Anand, Chithra, Chasins, Sarah E., Hellerstein, Joseph M., Kerrison, Erin Michelle Turner, Parameswaran, Aditya G.

In this paper we present techniques to incrementally harvest and query arbitrary metadata from machine learning pipelines, without disrupting agile practices. We center our approach on the developer-favored technique for generating metadata -- log st

Externí odkaz: http://arxiv.org/abs/2408.02498