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1,232 results on '"Tran, Huong"'

1. Mahonian Statistics and Vincular Patterns on Permutations over Multisets

Author

Ta, Lien T. P. and Tran, Huong T. T.

Subjects
Mathematics - Combinatorics
Abstract

Most Mahonian statistics can be expressed as a linear combination of vincular patterns. This is not only true with statistics on the permutation set, but it can also be applied for statistics on the permutation with repetition set. By following the method extending the vincular patterns combinations presented by Kitaev and Vajnovszki, we discover 8 vincular-patterns combinations of mad and madl extensions that are possible to be Mahonian. Some of these have been proved to be Mahonian on repetitive permutations by Clarke, Steingrimsson and Zeng, while the rest are new statistics extensions. In this thesis, we determine combinations of vincular pattern extension of mad and madl in Clarke, Steingrimsson and Zeng s paper, which have been proved to be Mahonian on the repetitive permutations. This result will be used to support the proof of Mahonity of the new statistics extensions. We show that these new statistics extensions are also Mahonian by constructing an involution {\Phi} on repetitive permutations, which preserves the descents statistics and transforms new statistics extensions to Mahonian mad and madl extensions of Clarke., Comment: 90 pages, 5 themas, bachelor thesis

Published
2024

2. Influences of Subjective Norms on Teachers' Intention to Use Social Media in Working

Author

Tran, Huong Thi Thu, Nguyen, Nga Thuy, and Tang, Thuy Thi

Abstract

This study investigates factors affecting teachers' intention to use the Zalo app--a social media with impressive users in Vietnam in recent years. The extended technology acceptance model (TAM) involves subjective norms (SNs) (colleagues, managers, students, and parents) and anxiety as the precursors of user attitude and intention to use as well as perceived ease of use (PEOU) and perceived usefulness (PU) as the key variables in TAM was employed. 1,105 teachers in Vietnam took part in the online survey. The study employed the partial least squares structural equation modeling (PLS-SEM) to analyze the quantitative data and the relationship among factors. The findings show that colleagues have no impact on PU, and managers have an insignificant influence on PEOU. In contrast, students and their parents positively influence teachers' PEOU and usefulness. Moreover, managers can increase teachers' anxiety levels, whereas students' connection decreases anxiety. These variables accounted for 79.6% of the variance in users' adoption. The results confirm the impact of SNs on teachers' intention to use the Zalo app in working. This is the study on issues in Vietnam related to social media used at institutional level--a no way back solution in the new educational context of the modern society.

Published
2023

4. Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions

Author

Chebotar, Yevgen, Vuong, Quan, Irpan, Alex, Hausman, Karol, Xia, Fei, Lu, Yao, Kumar, Aviral, Yu, Tianhe, Herzog, Alexander, Pertsch, Karl, Gopalakrishnan, Keerthana, Ibarz, Julian, Nachum, Ofir, Sontakke, Sumedh, Salazar, Grecia, Tran, Huong T, Peralta, Jodilyn, Tan, Clayton, Manjunath, Deeksha, Singht, Jaspiar, Zitkovich, Brianna, Jackson, Tomas, Rao, Kanishka, Finn, Chelsea, and Levine, Sergey

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

In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups. We therefore refer to the method as Q-Transformer. By discretizing each action dimension and representing the Q-value of each action dimension as separate tokens, we can apply effective high-capacity sequence modeling techniques for Q-learning. We present several design decisions that enable good performance with offline RL training, and show that Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite. The project's website and videos can be found at https://qtransformer.github.io, Comment: See website at https://qtransformer.github.io

Published
2023

5. RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Author

Brohan, Anthony, Brown, Noah, Carbajal, Justice, Chebotar, Yevgen, Chen, Xi, Choromanski, Krzysztof, Ding, Tianli, Driess, Danny, Dubey, Avinava, Finn, Chelsea, Florence, Pete, Fu, Chuyuan, Arenas, Montse Gonzalez, Gopalakrishnan, Keerthana, Han, Kehang, Hausman, Karol, Herzog, Alexander, Hsu, Jasmine, Ichter, Brian, Irpan, Alex, Joshi, Nikhil, Julian, Ryan, Kalashnikov, Dmitry, Kuang, Yuheng, Leal, Isabel, Lee, Lisa, Lee, Tsang-Wei Edward, Levine, Sergey, Lu, Yao, Michalewski, Henryk, Mordatch, Igor, Pertsch, Karl, Rao, Kanishka, Reymann, Krista, Ryoo, Michael, Salazar, Grecia, Sanketi, Pannag, Sermanet, Pierre, Singh, Jaspiar, Singh, Anikait, Soricut, Radu, Tran, Huong, Vanhoucke, Vincent, Vuong, Quan, Wahid, Ayzaan, Welker, Stefan, Wohlhart, Paul, Wu, Jialin, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Sichun, Yu, Tianhe, and Zitkovich, Brianna

Subjects
Computer Science - Robotics, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows that our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot training data (such as placing an object onto a particular number or icon), and the ability to perform rudimentary reasoning in response to user commands (such as picking up the smallest or largest object, or the one closest to another object). We further show that incorporating chain of thought reasoning allows RT-2 to perform multi-stage semantic reasoning, for example figuring out which object to pick up for use as an improvised hammer (a rock), or which type of drink is best suited for someone who is tired (an energy drink)., Comment: Website: https://robotics-transformer.github.io/

Published
2023

10. Vietnamese Teachers' Acceptance to Use E-Assessment Tools in Teaching: An Empirical Study Using PLS-SEM

Author

Tang, Thuy Thi, Nguyen, Thuy Nga, and Tran, Huong Thi Thu

Abstract

The purpose of this study is to examine factors that influence teachers' intentions to use technology in assessments using the technology acceptance model (TAM) as a framework. An online survey was utilized to collect data, and 360 teachers participated in the survey. This study used partial least squares-structural equation modelling (PLS-SEM) to test the hypotheses to verify the effects of variables on teachers' intention of e-assessment use. The model consists of four constructs: computer self-efficacy (CE), perceived ease of use (PEOU), perceived usefulness (PU), and frequent use of e-assessment tools (FoUAT). The findings revealed a significant influence path from CE to PEOU, FoUAT, and behavior intention. In addition, PEOU is a critical factor that positively impacts both PU and teachers' behavior intentions. In contrast to our expectation, frequency of use was statistically insignificant and had no impact on teachers' intention to use (IU) e-assessment tools. The total of these four variables corresponded to 71.4% of the variance of user intention. These results confirm that TAM is an effective model to explain teachers' technology acceptance to use e-assessment tools for their teaching.

Published
2022

11. RT-1: Robotics Transformer for Real-World Control at Scale

Author

Brohan, Anthony, Brown, Noah, Carbajal, Justice, Chebotar, Yevgen, Dabis, Joseph, Finn, Chelsea, Gopalakrishnan, Keerthana, Hausman, Karol, Herzog, Alex, Hsu, Jasmine, Ibarz, Julian, Ichter, Brian, Irpan, Alex, Jackson, Tomas, Jesmonth, Sally, Joshi, Nikhil J, Julian, Ryan, Kalashnikov, Dmitry, Kuang, Yuheng, Leal, Isabel, Lee, Kuang-Huei, Levine, Sergey, Lu, Yao, Malla, Utsav, Manjunath, Deeksha, Mordatch, Igor, Nachum, Ofir, Parada, Carolina, Peralta, Jodilyn, Perez, Emily, Pertsch, Karl, Quiambao, Jornell, Rao, Kanishka, Ryoo, Michael, Salazar, Grecia, Sanketi, Pannag, Sayed, Kevin, Singh, Jaspiar, Sontakke, Sumedh, Stone, Austin, Tan, Clayton, Tran, Huong, Vanhoucke, Vincent, Vega, Steve, Vuong, Quan, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Sichun, Yu, Tianhe, and Zitkovich, Brianna

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer1.github.io, Comment: See website at robotics-transformer1.github.io

Published
2022

16. Measurement of circulating viral antigens post-SARS-CoV-2 infection in a multicohort study

Author

Abraham, Rachael, Ager, Arijan, Aguilar, Franchesca A., Ahmadi-Izad, Ghazal, Ahmed, Dilshad R., Alvarez, Alicarmen, Anderson, Blake, Asencios, Walter D., Atha, Mary, Beaty, Casey L., Bedi, Brahmchetna, Berry, Jasmine A., Boone, Donchel, Bower, Mary, Bremner, James D., Brent, Corbin, Brown-Smith, Ke'Ara, Bull, Rachel, Bush, Patricia A., Capo, Gustavo, Carl-Igwe, Kelechi, Chitadze, Calista, Chukwumerije, Nachi, Clyburn, Erna, Collins, Shelby, Costello, Julie, Couture, Grace, Craft, Angel, Cribbs, Sushma K., Cui, Xiangqin, Dandy, Annette, Rio, Carlos del, Jasarevic, Rijalda, Detelich, Joshua F., Dixon, Cartia, Dow, Jeanne, Doyle, D'Andrea, Elchommali, Jannah, Ibeawuchi, Carmel, Elsey, Imani, Fineman, Rebecca, Francis, Anyssa G., Franks, Nicole, Gallini, Julia, Gander, Jennifer C., Gray, Natalie, Grimes, Ash, Gutter, Evan, Han, Jenny E., Hang, Tina P., Harding, Jess, Hernandez, Liliana, Hewitt, Lauren N., Holloway, Carla, Hudgins, Alex F., Huerta, Christopher, Ifejika, Cynthia, Ingram, Ketteria D., Javia, Vidhi N., Jeter, Mykayla, Johnson, Brandi, Joseph, Yasha, Juarez, Monica, Kajan, Dana, Khalil, Lana, Kirkpatrick, Caitlin M., Kleinhenz, Dean, Kolailat, Imanii, Koumanelis, Athena, Koumanelis, Alexandra, Kozoman, Rebecca, Krishnan, Shilpa, Lainez, Jordi, Lawrence, Brianna, Lee, Matthew A., Leon, Jose D., Lew, Valerie, Lewis, Kennedy C., Litvack, Matthew, Maroney, Mara, Maier, Cheryl L., Makkaoui, Nour, Marconi, Vincent C., Martin, Christopher F., Martinez, Monica, Mbogo, Loice, McCaslin, Atuarra, McIntyre, Jerrod, Moanna, Abeer, Montoya, Miranda, Morales, Elena, Moran, Caitlin A., Morgan-Billingslea, Jan, Murray, Calista, Nelson, Roslin, Neuman, Robert B., Nguyen, Tran, Ofotokun, Ighovwerha, Ojemakinde, Elizabeth I., Ojoawo, Bukkie, Osinski, Eileen, Oviedo, Sofia, Panganiban, Bernadine, Paredes-Gaitan, Yolanda, Patzer, Rachel E., Pemu, Priscilla, Prude, Michael, Rahman, Kazi, Ramakrishnan, Grace, Rebolledo, Paulina A., Roberts, Marjorie, Robinson, Keysha, Rogers, Chantrice, Rouphael, Nadine G., Searles, Charles, Shah, Anand, Segall, Marni, Shaw, Renata M., Silva, Ruvina, Simpson, Cheryl, Simpson-Derrell, Krystal, Sirajud-Deen, Talib, Smith, Veronica E., Stringer, Andre, Stroud, Jacob, Suthar, Mehul S., Sylber, Cory, Sylvera, Ashley, Tanner, Tehquin, Teunis, Larissa J., Tolbert, Maliya, Thomas, Kodasha M., Thompson, Sierra G., Titanji, Kehmia, Toy, Christopher, Traenkner, Jessica, Truong, Alex, Unterberger, Kristen, Vaccarino, Viola, Varney, Kris, Vyas, Kartavya, Vyas, Kurt, Walker, Tiffany A., Walkow, Max, Wang, Dongli, Wesley, Tamara, Wiley, Zanthia, Wimberly, Erika, Winston, Juton R., Winter, Terra J., Wongtrakool, Cherry, Aikawa, Masanori, Alba, George A., Aung, Taing N., Baden, Lindsey, Baslet, Gaston, Bassett, Ingrid V., Bennett, Lindsey, Bhattacharyya, Shamik, Blazey-Martin, Deborah, Buring, Julie, Cagnina, Rebecca E., Chen, Li Qing, Clark, Cheryl R., Cohen, Pieter, Collier, Ai-Ris, Czeisler, Charles, Duffy, Elizabeth, Estill, Peter, Fong, Tamara, Gay, Elizabeth, Ghamloush, Maher, Ginns, Leo C., Haack, Monika, Haas, Jennifer, Hamburg, Naomi, Hauser, Kristine S., John, Janice, Jordan, Michael, Juelg, Boris D., Kanjilal, Diane G., Kim, Arthur Y., Klerman, Elizabeth B., Kobayashi, Misaki ., Kogelman, Laura, Lamas, Daniela, Levy, Bruce D., Levy-Carrick, Nomi, Lewis, Gregory, Maley, Jason H., Manson, JoAnn, Marathe, Jai G., Mullington, Janet M., O'Connor, George T., Ojikutu, Bisola, Perlis, Roy, Quintana, Yuri, Redline, Susan, Remis, Elijah J., Rosand, Jonathan, Sesso, Howard D., Shaughnessy, Lynn, Shepherd, Fitzgerald M., Solomon, Scott, Sparks, Jeffrey A., Spencer, Lia L., Stephenson, Kathryn, Systrom, David, Thomas, Robert J., Min Thu, Phyo Phyo, Ticotsky, Amberly, Torres, Robert, Wallace, Zachary S., Walt, David, Ward, Honorine D., Washko, George, Whittelsey, Maureen, Wiener, Rebecca, Williams, Charles T., Xerras, Dean, Zhang, Haihua, Zionts, Danielle, Armstrong, Donna, Binkley, Susan E., Blackwell, Kenneth, Brown, Todd, Carton, Thomas W., Causey, Annalia, Cook, Felice, Daniel, Casey L., Datri, Paula, Domingo, Julio, Donahue, Conner, Eady, Maitlyn, Edberg, Jeffrey, Erdmann, Nathaniel, Fuloria, Jyotsna, Garcia-McClaney, Noah, Garner, Melissa, Gillespie, Mark, Gray, Brandon, Hagensee, Michael, Hall, Wanda, Hansel, Jamie, Hart, Cady, Hebson, Camden L., Hidalgo, Bertha, Holtzapfel, Kaylen, Jinright, Alexis, Judd, Suzanne E., Kennedy, Teri, Kirkwood, Leigh, Leggio, Cathryn, Levitan, Emily B., Maier, Megan, McCormack, Patricia, Miele, Lucio, Mitchell, Kevin, Montgomery, Aoyjai, Peralta-Carcelen, Myriam, Perkins, Allen, Pilco, Juan P., Powell, Leigh, Shevin, Rachael, Skipworth, Sidney, Spurgeon, Leah, Sutherland, Erica, Tita, Alan T., Trauth, Amber, Trotter, Siobhan, Van Deerlin, Alexander, Ware, Gregory, Weiser, Sharon, Wilson, Rosanne, Woodruff, Dana, Wu, Jing, Young, Madeline, Alemu, Mhret, Anderson, Jordan, Ashktorab, Hassan, Brim, Hassan, Chang, Linda, Chauhan, Mahak, Cho, Sung, Durrani, Saima, Gentil, Monique P., Goodman, Karli, Laiyemo, Adeyinka O., Lanke, Gandi, Lebron, Ralph, Maheshwari, Anurag, Mehari, Alem, Nezamloo, Ali, Ngwa, Julius, Njoku, Noelle, Ok, Jina, Sherif, Zaki A., Solemani, Akbar, Thuluvath, Paul, To, Chau, Spikes, Leslie A., James, Judith A., Luciano Roman, Carlos A., Chow, Dominic C., Marshall, Gailen D., Dickinson, John D., Hoover, Susan E., Warren, David E., Emery, Ivette F., Sukhera, Fatima I., Rosen, Clifford J., Greenway, Frank L., Hodder, Sally L., Shikuma, Cecilia M., VanWagoner, Timothy M., Bardes, James M., Kirwan, John P., Wood, Jeremy P., Whiteheart, Sidney W., Shellito, Judd, Roelke, Theresa, Black, Lora, Tjarks, Brian, Fonseca, Vivian, Gupta, Shaveeta, Longo, Michele, Yang, Mei, MarGangcuangco, Louis, Bengtson, Charles, Castro, Mario, Howard, Theresa, Garvy, Beth, Simmons, Christopher, Garla, Vishnu, Kuebler, Joy, Nandi, Utsav, Vasey, Andrew, Bogie, Amanda, Scott, James, Frontera, Sigrid Perez, Bagur, Jorge Santana, Dominique-Villanueva, Daphne, Juskowich, Joy, Reece, Rebecca, Sarwari, Arif, Aponte-Soto, Lisa, Adams, Dara, Baker, Aileen, Barbera, Sunni, Basu, Sanjib, Bleasdale, Susan, Bolliger, Dawn, Boyd, Andrew D., Boyineni, Jerusha, Breiter, Taylor, Brown, Daniel, Buhimschi, Irina A., Carrithers, Michael D., Certa, Marta, Chalamalla, Rashmika, Chebrolu, Praneeth, Chestek, David, Chessier, Erica, Cook, Judith A., Cranford, Savannah, Curry, Hannah L., Darbar, Dawood, Dasgupta, Raktima, Blakley, Felicia Davis, DeLisa, Julie A., Del Rios, Marina, Diaz, Maya Z., Diviak, Kathleen R., Dixon, Jennifer, Donlon, Meghan F., Donohue, Sarah E., Dworkin, Mark S., Edmonds, Sherrie, Ellison, Angela, Everett, Emily, Flanigan, Clarie, Freedman, Michael B., Gale, Lisa, Gerald, Lynn B., Giles, Wayne H., Gordon, Howard S., Hafner, John, Hammad, Bayan, Hanson, Keith A., Harris, Pastor C., Hartwig, Kimberly, Hasek, Sharon, Hasse, Wendy, Hendrickson, Monica, Hobbs, Brianna, Hryniewicka, Martyna, Hammerl, Savannah, Hutton, Robert, Ibanez, Alejandra L., Illendula, Sai D., Ismail, Nahed, Jain, Akash, Jennette, Kyle J., Kadubek, Grace, Kent, Denise, Kotini-Shah, Pavitra, Kelly, Sara W., Kent, Denise A., Kim, Keri S., Kindred, Elijah, Klein, Jonathan D., Krishnan, Jerry A., Large, Lucia, Lash, James ., Lin, Janet Y., Lu, Jun, Mahamed, Abeer M., Maholovich, Phoebe, Malchenko, Sergey, Martinez, Miriam, Mauntel-Medici, Cammeo, Madineni, Abhigna, McCauley, Mark, Menchaca, Martha, Mermelstein, Robin, Moreno, David J., Morrissy, Liam, Muramatsu, Naoko, Musick, Hugh, Noland, Seth, Norwick, Lourdes, Novak, Richard M., Olds, Lela, Ortiz, Marilyn, Patel, Khushboo, Perez, Nicolas L., Pliskin, Neil H., Pope, Sam, Prabhakar, Bellur S., Prasad, Bharati, Predki, Barbara, Prendergast, Heather M., Quigley, John G., Ramchandran, Ramaswamy, Ramirez, Ana, Rappe, Sarah, Rehman, Jalees, Rolon, Cesar, Rowley, Matthew, Rudraraju, Gowrisree, Rutherfoord, Melissa, Sader, Samer B., Sculley, Jennifer A., Smith-Mack, Jerisha, Swearingen, Peyton, Stewart de Ramirez, Sarah A., Sudhindra, Praveen, Sun, Jun, Tartt, Nancy, Terlinde, Tracy, Thompson, Tiffany, Vanden Hoek, Terry L., Kelly, Sara Warfield, Villanueva, Laura, Welter, Hannah, Woolley, Brittany, Yazici, Cemal, Charney, Alexander W., Kovatch, Patricia, Merad, Miriam, Nadkarni, Girish N., Wisnivesky, Juan P., Aberg, Judith A., Ascolillo, Steven, Assenso, Esther, Bagiella, Emilia, Bartram, Logan, Becker, Jacqueline, Beckmann, Noam D., Bendl, Ashley, Chen, Benjamin K., Civil, Alyssa, Dhar, Kaberi, Evo-Ortega, Lorraine, Fierer, Daniel, Gallagher, Emily J., Garcia-Sastre, Adolfo, Gnjatic, Sacha, Guliyeva, Sabina, Harvey-Ingram, Lori, Herrera-Moreno, Julia, Hill, Matthew, Horowitz, Carol R., Jackson, Rachel, Kastrat, Din, Lala-Trindade, Anu, Lin, Jenny, Macaluso, Nick, Marcon, Kathryn, Meyer, Dara, Morinigo, Janice, Natelson, Benjamin H., Nussenzweig, Maya, Padua, Tiffani, Putrino, David, Quazi, Nawreen, Ramos, Michelle, Richardson, Lynne, Russo, Scott, Seifert, Alan C., Serri, Abdullah, Walker, Jordan, Yee, Michell, Adolphi, Natalie L., Alekhina, Natalya, Archuleta, David A., Barlocker, Jackson, Bateman, Lucinda, Bradfute, Steven B., Brito, Rebecca, Bryan, Tanner W., Buck, Kaitlin E., Davis, Debra, Deakyne Davies, Sara J., Decker, Lauren A., Elifritz, Jamie, Erlandson, Kristine M., Facelli, Julio C., Fudge, Harrison Z., Tran, Huong, Pitch, Chloe, Feuerriegel, Elen M., Ford, Isaac, Friedman, Naomi P., Garcia-Soberanez, Noella D., Gardner, Edward M., Stringham, Caitlyn, Ling, Leah, Gebremariam, Tewodros H., Gentry, Frederick D., Gouripeddi, Ramkiran, Graham, Paige, Gronert, Eve G., Harkins, Michelle S., Hawkins, Kellie L., Hess, Rachel, Johnny, Jace D., Johnson, Brandon M., Jolley, Sarah E., Lloyd, Jennifer, Ludwig, Katelyn R., Martinez, Noah I., McCandless, Sean A., Montoya, Lorenzo A., Oakes, Judy L., Parada, Alisha N., Quinn, Davin K., Raissy, Hengameh, Ramos, Alfredo, Reid, Kayleigh M., Reusch, Jane E., Sheehan, Elyce B., Sokol, Ron J., Treacher, Irena S., Trinity, Joel D., Truong, Dongngan T., West, Shelby C., Molden, Joie, Sharareh, Nasser, Weaver, Lisa J., Spivak, Adam M., Brown, Jeanette P., Shah, Kevin S., Pace, Laura A., Scholand, Mary Beth, Velinder, Matt, Cortez, Melissa, Morimoto, Sarah Shizuko, Vernon, Suzanne D., Lu, Yue, Owen, Megan, Hermansen, Jessica A., Lindsay, Ashton M., Donohue, Dagny K., Garg, Lohit, Wodushek, Thomas, Higgins, Janine, Lockie, Tim, Brightman, Marisa, Thurman, Brook, Powell, Jenny M., Freston, Dylan C., Medina, Juliemar C., Aguirre, Bailee, Anderson, Jeff, Bair, Tami, Bosh, Lindsay, Evans, Lorlie, Garrett, Chase, Harris, Dixie, Herrera, Katherine, Horne, Benjamin D., Juan, James, Knight, Stacey, Knowlton, Kirk, Leither, Lindsay, Maestas, Heather, May, Heidi T., Najarian, Gabriel, Woller, Scott C., Zubal, Shyanne, Jensen, McKenna M., Webb, Tiaura, Iverson, Leslie, Ayache, Mirna, Baloi, Alexis, Barnboym, Emma, Boldt, Nicholas, Bukulmez, Hulya, Chesnick, Hailey, Conrad, Ann, Consolo, Mary, Curtis, Lynette, D’anza, Brian, DiFrancesco, Kathryn, Edminston, Marissa, Eteshola, Ebenezer, Gallagher, Michelle, Gibson, Kelly S., Gordesky, Larraine, Greenwood, Carla, Haghiac, Maricela, Harris, Paul, Hernandez, Carla, Iqbal, Shirin, Kaelber, David C., Kaufman, Elizabeth S., Kennedy, Olivia, Labbato, Danielle, Lengu, Ketrin, Levert, Antonio, Levin, Jennifer, Lowenthal, Rebecca, Mackin, Bridget, Malakooti, Shahdi K., McComsey, Grace A., Minium, Judy, Mouchati, Christian, Oleson, Christine, Pearman, Ann, Hershey, Morgan, Rivera, Amanda, Rodgers, Michael, Rodgers, Theresa, Roy, Arnab, Russ, Kris, Scott, Sarah, Sheth, Niyati, Singer, Nora G., Smith, Beth, Smith, Cheryl, Stancin, Terry, Temple, Daniel, Tribout, Megan, Weinberger, Elisheva, Zhang, David, Zisis, Sokratis N., Atieh, Ornina, Yendewa, George, Baissary, Jhony, Pettinato, KImberly, Lim, Joaquin, Jacob, Joshua, Adams, Cara, Tejani, Viral, Algren, Heather A., Alicic, Radica, Baxter, Joni, Brennan, Conor, Caudill, Antonina, Chen, Peter, Chopra, Tananshi, Chu, Helen Y., Del Alcazar, James, Duven, Alexandria M., Edmark, Rick, Emerson, Sarah, Goldman, Jason D., Gutierrez, Vanessa, Hadlock, Jennifer, Harteloo, Alex, Heath, James R., Hood, Susan, Jackman, Susan, Kaneko, John, Kemp, Megan, Kim, Christina, Kuykendall, Kelli, Li, Sarah, Logue, Jennifer K., Magis, Andrew T., Manner, Paula, Mason, Carly, McCaffrey, Kathryn, McDonald, Connor, McDonald, Dylan, Murray, Kim M., Nackviseth, Callista, Nguyen, Helen, Parimon, Tanyalak, Poussier, Rachel, Rowen, Lee, Satira, Richard, Torbati, Sam, Tuttle, Katherine R., Wallick, Julie A., Yuan, Dan, Watanabe, Kino, Wilcox, Lauren E., Contreras, Fatima, Dahlke, Lea, Gudipudi, Lasya, Modes, Matthew, Muttera, Nicole, Salinas, Nancy, Tadeo, Josie, White, Shane, Alvarado, Stephanie, Anderson, Reed, Arellanes, Azaneth, Barajas, Rose A., Chauhan, Suneet P., Clarke, Geoffrey D., Farner, Cheryl E., Fischer, Melinda S., Goldberg, Mark P., Hasbani, Keren, Hastings, Gabrielyd, Heard, Patricia, Herrera, Italia, Infante, Edgar, Johnson, Hillary, Jones, Johnnie, Kellogg, Dean L., Kraig, Ellen, Longoria, Lisa, Nambiar, Anoop M., Okafor, Emeka, Paredes, Claudia C., Patterson, Thomas F., Patterson, Jan E., Pinones, Alexis, Potter, Jennifer S., Reeves, W.B., Saade, George R., Salehi, Marzieh, Scholler, Irma, Seshadri, Sudha, Shah, Dimpy P., Shah, Pankil, Sharma, Kumar, Sharma, Kavita, Soileau, Bridgette, Solis, Pamela, Stoebner, Carmen, Sullivan, Michael, Taylor, Barbara S., Tragus, Robin, Tsevat, Joel, Verduzco-Gutierrez, Monica, Ahuja, Neera, Blish, Catherine A., Blomkalns, Andra L., Bonilla, Hector, Brotherton, Richard, Clinton, Kimberly, Dingankar, Vaidehi, Geng, Linda N., Go, Minjoung, Haddad, Francois, Jagannathan, Prasanna, Jamero, Christopher, Jee, Kathryn, Jia, Xiaolin K., Khurana, Naresh, Kumar, Andre, Maldonado, Yvonne, Miglis, Mitchell G., O'Conor, Ellen, Olszewski, Kelly, Pathak, Divya, Quintero, Orlando, Scott, Jake, Singh, Upinder, Urdaneta, Alfredo E., Utz, Paul J., Varkey, Mary R., Saperia, Corey, Autry, Lynn, Bime, Christian, Borwege, Sabine, Copeland, Jacquelynn, DiLise-Russo, Marjorie, Ernst, Kacey C., Esquivel, Denise R., Fadden, Susan, Gomez, Isaias, Grischo, Garrett, Hansen, Lillian, Harris, David T., Harris, Stefanie, Hartley, William, Hernandez, Michael, Hillier, Leah, Hsu, Harvey, Hughes, Trina, Ismail, Hira, Iusim, Stephanie, James, Michelle, Kala, Mrinalini, Karnafel, Maria, Kim, Daniel, Knox, Kenneth S., Koleski, Alison, LaFleur, Bonnie, Lambert, Brenda, LaRue, Sicily, Lee-Iannotti, Joyce K., Lieberman, David, Lutrick, Karen, Merchant, Nirav, Morton, Christopher, Mosier, Jarrod M., Murthy, Ganesh, Nikolich, Janko Z., Olorunnisola, Toluwanimi, Parthasarathy, Sairam, Peralta, Jeanette, Pilling, William, Pogreba-Brown, Kristen, Reiman, Eric M., Rischard, Franz P., Ryan, Lee T., Smith, Terry, Snyder, Manuel, Soto, Francisco, Subbian, Vignesh, Suhr, Kyle, Unzek, Samuel, Vadovicky, Sheila, Velarde, Deanna, Veres, Sharry, Wilson, Cathleen, Anderson, Grace, Anglin, Khamal, Argueta, Urania, Asare, Kofi, Buitrago, Melissa, Chang Song, Celina, Clark, Alexus, Conway, Emily, Deeks, Steven G., Del Castillo, Nicole, Deswal, Monika, Durstenfeld, Matthew S., Eilkhani, Elnaz, Eun, Avery, Fehrman, Emily, Figueroa, Tony, Flores, Diana, Grebe, Halle, Henrich, Timothy J., Hoh, Rebecca, Hsue, Priscilla, Huang, Beatrice, Ibrahim, Rania, Kelly, John D., Kerbleski, Marian, Kirtikar, Raushun, Lew, Megan T., Lombardo, James, Lopez, Monica, Luna, Michael, Marquez, Carina, Martin, Jeffrey N., Munter, Sadie, Ngo, Lynn, Peluso, Michael J., Pineda-Ramirez, Jesus, Rhoads, Kim, Rodriguez, Antonio, Romero, Justin, Ryder, Dylan, So, Matthew, Somsouk, Ma, Tai, Viva, Tran, Brandon, Uy, Julian, Valdivieso, Daisy, Verma, Deepshika, Williams, Meghann, Zamora, Andhy, Newman, Lisa T., Abella, Julie, Barnette, Quinn, Bevc, Christine, Beverly, Jennifer, Ceger, Patricia, Croxford, Julie, Enger, Mike, Fain, Katie, Farris, Tonya, Hanlon, Sean, Hines, David, Johnson-Lawrence, Vicki, Jordan, Kevin, Lefebvre, Craig, Linas, Beth, Luukinen, Bryan, Mandal, Meisha, McKoy, Nikki J., Nance, Susan, Pasquarelli, Demian, Quiner, Claire, Sembajwe, Rita, Shaw, Gwendolyn, Thornburg, Vanessa, Tosco, Kendall, Wright, Hannah, Gross, Rachel S., Hochman, Judith S., Horwitz, Leora I., Katz, Stuart D., Troxel, Andrea B., Adler, Lenard, Akinbo, Precious, Almenana, Ramona, Aschalew, Malate, Balick, Lara, Bello, Ola, Bhuiyan, Sultana, Blachman, Nina, Branski, Ryan, Briscoe, Jasmine, Brosnahan, Shari, Bueler, Elliott, Burgos, Yvette, Caplin, Nina, Chaplin, Domonique, Chen, Yu, Cheng, Shen, Choe, Peter, Choi, Jess, Chung, Alicia, Church, Richard, Cobos, Stanley, Croft, Nakia, Irving, Angelique Cruz, Del Boccio, Phoebe, Díaz, Iván, Divers, Jasmin, Doshi, Vishal, Dreyer, Benard, Ebel, Samantha, Esquenazi-Karonika, Shari, Faustin, Arline, Febres, Elias, Fine, Jeffrey, Fink, Sandra, Freeland, Catherine, Frontera, Jennifer, Gallagher, Richard, Gonzalez-Duarte, Alejandra, Gross, Rachel, Hasson, Denise, Hill, Sophia, Hochman, Judith, Horwitz, Leora, Hossain, Jennifer, Islam, Shahidul, Jean, Christina Saint, Johnson, Stephen, Kansal, Neha, Katz, Stuart, Kenney, Rachel, Kershner, Tammy, Kewlani, Deepshikha, Kwak, Judy, Lamendola-Essel, Michelle F., Laury, Sarah, Laynor, Gregory, Lei, Lei, Leon, Terry, Linton, Janelle, Logan, Max, Malik, Nadia, Mamistvalova, Lia, Mandel, Hannah, Maranga, Gabrielle, Mattoo, Aprajita, Mei, Tony, Mendelsohn, Alan, Mercier, Emmanuelle, Vernetti, Patricio Millar, Miller, Marc, Mitchell, Maika, Moreira, Andre, Mudumbi, Praveen C., Nahin, Erica, Nair, Nandini, Nekulak, Joseph, Owens, Kellie, Parent, Brendan, Patibandla, Nandan, Petrov, Peter, Postelnicu, Radu, Pratt, Francesca, Randall, Isabelle, Rao, Priyatha, Rapkiewicz, Amy, Rizzo, JohnRoss, Rosas, Johana, Rose, Chelsea, Saint-Jean, Christina, Santacatterina, Michelle, Shah, Binita, Shaukat, Aasma, Simon, Naomi, Simsir, Aylin, Stinson, Miranda, Tang, Wenfei, Tatapudi, Vasishta, Thawani, Sujata, Thomas, Mary, Thorpe, Lorna, Tom, MeeLee, Treiha, Ethan, Troxel, Andrea, Truong, Jennifer, Udosen, Mmekom, Valencia, Carlos, Velazquez-Perez, Jessica, Vernetti, Patricio M., Vidal, Crystal, Viswanathan, Anand, Willerford, Amy, Williams, Natasha, Wong, Crystal, Wood, Marion J., Wuller, Shannon, Yin, Shonna H., Young, Chloe, Zaretsky, Jonah, Zavlunova, Susanna, Ahirwar, Shreya, Ahmed, Shifa, Ainsworth, Layne L., Atchley-Challenner, Rachel, Avilach, Paul, Balan, Trisha T., Benik, Nicholas, Benoit, Barbara, Bind, Marie-Abèle C., Bonaventura, William J., Boutin, Natalie, Brion, Beverly, Cagan, Andrew, Cai, Tianrun, Cao, Tingyi, Castro, Victor M., Cerretani, Xander R., Chan, James G., Cheng, David, Chibnik, Lori B., Ciriello, Mark, Costenbader, Karen, Dimitrov, Dimitar S., Estiri, Hossein, Fayad, Maria, Feldman, Candace H., Foulkes, Andrea, Gainer, Vivian, Ghosh, Bhaswati, Gollub, Randy, Guan, Zoe, Harris, Alan, Helmer, Karl, Hendrix, Andrew, Holzbach, Ana, Huang, Weixing, Karlson, Elizabeth W., Kaufman, Daniel, Keogh, Diane, Kerr, James D., Klann, Jeffrey G., Krishnamoorthy, Aparna, Lasky-Su, Jessica A., Liao, Katherine P., MacFadden, Doug, Maram, Anupama, Martel, Megan W., Mendis, Michael, Metta, Reeta, Monteiro, Jonathan, Morales, Eduardo, Morse, Richard E., Murphy, Shawn, Nazaire, Marc-Danie, Neils, Gregory, Nguyen, Amber N., Norman, James, Paik, Henry H., Pant, Deepti, Park, HeeKyong, Rabideau, Dustin J., Reeder, Harrison T., Rossi-Roh, Kathleen, Santacroce, Leah M., Schlepphorst, Katherine, Schulte, Carolin, Selvaggi, Caitlin A., Shinnick, Daniel J., Simons, William, Simpson, Lynn A., St. Jean Flanders, Mary L., Strasser, Zachary, Thakrar, Mansi R., Thaweethai, Tanayott, Thorn, Madeleine, Trewett, Philip, Van Fleet, Dustin, Wagholikar, Kavishwar B., Wang, Taowei D., Wattanasin, Nich, Weber, Griffin, Williams, Michael A., Zhang, Ren Zhe, Cicek, Mine, Chang, Nancy, Wirkus, Samantha, Zahnle, Nicole, Flotte, Thomas J., Frisch, Erika, Boysen, Erik M., Welch, Gary, Akintonwa, Teresa, Blancero, Frank, Brown, Heather-Elizabeth, Carmilani, Megan, Cerda, Marta, Clash, Victor H., Copeland, Debra, Hall, Yvonka, kevin kondo, Lerma, Lydia, Lindsay, Jacqui, Marti, Heather, Maughan, Christine, Minor, Tony, Taylor, Brittany, Vincent, Hyatt, Zissis, Mike, Anderson, Brett, Bardhan, Sujata, Castro-Baucom, Leah, Chisolm, Deena, Corchado, Claudia, Damian, April Joy, Daniel, Casey, DasGupta, Soham, Dehority, Walter, Feldman, Candace, Fessel, Josh, Rosas, Lisa Goldman, Horowitz, Carol, Khullar, Dhruv, Lopez, Keila, McDonald Pinkett, Shelly, Myaskovsky, Larissa, Regino, Lidia, St John Thomas, Gelise, Stewart de Ramirez, Sarah, Vangeepuram, Nita, Walden, Anita, Williams, Neely, Yin, Shonna, Burton, Phoebe, Catallozzi, Marina, Clark, Cheryl, Dworetzky, Beth, Edwards, Belinda, Ferrer, Robert L., Judd, Suzanne, Rothman, Russell, Wagner, Laura, Wallace, Ann, Adams, Sonseeahray (Ray), Aragon, Leyna, Bander, Bryan, Bishof, Karyn, Brooks, Gail, Carignan, Etienne, Coombs, Krista, Davis, Hannah, Blakley, Felicia D., Diggs, Marissa, Brown, Heather E., Favors, Umar, Fields, Whitney, Fisher, Liza, Fitzgerald, Megan, Gaffney, Alicia, Witvliet, Margot Gage, Garcia, Roberto, Gustafson, Tyler, Guthe, Nick, Holmes, Verna, Hornig, Mady, Hornig, Maxwell, Jefferson, Wendy, Kochis, Nancy, Kondo, Kevin, Lam, Julie, Lawrence, Fadwa, Letts, Rebecca, Lewis, Juan, Lopez, Silcia, Martinez, Thomas, McCorkell, Lisa, McGrath, Rebecca, Minor, Thomas T., Moore, Charita, Nguyen, Kian, Nichols, Lauren, O'Brien, Lisa, Olson, Holly, Peddie, Aimee, Perlowski, Alice, Lorenzo, Elizabeth P., Prentiss, Lisa, Raytselis, Nadia, Rochez, Nitza, Rockwell, Megan, Rutter, Jacqueline, Seibert, Elle, Sekar, Anisha, Smith, Chimere, Stiles, Lauren, Taylor, Emily, Thompson, Julie, Trapp, Stephen, Valdiva, Stephen, Wilensky, Rochelle, Williams, Melissa, Dawson, Kay W., Wylam, Andrew, Swank, Zoe, Borberg, Ella, Chen, Yulu, Senussi, Yasmeen, Chalise, Sujata, Manickas-Hill, Zachary, Yu, Xu G., Li, Jonathan Z., Alter, Galit, Kelly, J. Daniel, Goldberg, Sarah A., Talla, Aarthi, Li, Xiaojun, Skene, Peter, Bumol, Thomas F., Torgerson, Troy R., Czartoski, Julie L., McElrath, M. Juliana, and Walt, David R.

Published
2024
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22. Electrically injected GeSn lasers with peak wavelength up to 2.7 micrometer at 90 K

Author

Zhou, Yiyin, Ojo, Solomon, Miao, Yuanhao, Tran, Huong, Grant, Joshua M., Abernathy, Grey, Amoah, Sylvester, Bass, Jake, Salamo, Gregory, Du, Wei, Liu, Jifeng, Margetis, Joe, Tolle, John, Zhang, Yong-Hang, Sun, Greg, Soref, Richard A., Li, Baohua, and Yu, Shui-Qing

Subjects
Physics - Applied Physics
Abstract

GeSn lasers enable monolithic integration of lasers on the Si platform using all-group-IV direct-bandgap materials. Although optically pumped GeSn lasers have made significant progress, the study of the electrically injected lasers has just begun only recently. In this work, we present explorative investigations of electrically injected GeSn heterostructure lasers with various layer thicknesses and material compositions. The cap layer total thickness was varied between 240 and 100 nm. At 10 K, a 240-nm-SiGeSn capped device had a threshold current density Jth = 0.6 kA/cm2 compared to Jth = 1.4 kA/cm2 of a device with 100-nm-SiGeSn cap due to an improved modal overlap with the GeSn gain region. Both devices had a maximum operating temperature Tmax = 100 K. Device with cap layers of Si0.03Ge0.89Sn0.08 and Ge0.95Sn0.05, respectively, were also compared. Due to less effective carrier (electron) confinement, the device with a 240-nm-GeSn cap had a higher threshold Jth = 2.4 kA/cm2 and lower maximum operating temperature Tmax = 90 K, compared to those of the 240-nm-SiGeSn capped device with Jth = 0.6 kA/cm2 and Tmax = 100 K. In the study of the active region material, the device with Ge0.85Sn0.15 active region had a 2.3 times higher Jth and 10 K lower Tmax, compared to the device with Ge0.89Sn0.11 in its active region. This is likely due to higher defect density in Ge0.85Sn0.15 rather than an intrinsic issue. The longest lasing wavelength was measured as 2682 nm at 90 K. The investigations provide guidance to the future structure design of GeSn laser diodes to further improve the performance., Comment: 21 pages, 5 figures, and 2 tables

Published
2020

23. Electrically injected GeSn lasers on Si operating up to 100 K

Author

Zhou, Yiyin, Miao, Yuanhao, Ojo, Solomon, Tran, Huong, Abernathy, Grey, Grant, Joshua M., Amoah, Sylvester, Salamo, Gregory, Du, Wei, Liu, Jifeng, Margetis, Joe, Tolle, John, Zhang, Yong-Hang, Sun, Greg, Soref, Richard A., Li, Baohua, and Yu, Shui-Qing

Subjects
Physics - Applied Physics
Abstract

The significant progress of GeSn material development has enabled a feasible solution to the long-desired monolithically integrated lasers on the Si platform. While there are many reports focused on optically pumped lasers, GeSn lasers through electrical injection have not been experimentally achieved yet. In this work, we report the first demonstration of electrically injected GeSn lasers on Si. A GeSn/SiGeSn heterostructure diode grown on a Si substrate was fabricated into ridge waveguide laser devices and tested under pulsed conditions. Special considerations were given for the structure design to ensure effective carrier confinement and optical confinement that lead to lasing. Lasing was observed at temperatures from 10 to 100 K with emission peaks at around 2300 nm. The minimum threshold of 598 A/cm2 was recorded at 10 K and the threshold increased to 842 A/cm2 at 77 K. The spectral linewidth of a single peak was measured as small as 0.13 nm (0.06 meV). The maximum characteristic temperature was extracted as 99 K over the temperature range of 10-77 K.

Published
2020

27. Radiomics could predict surgery at 10 years in Crohn's disease

Author

Laterza, Lucrezia, Boldrini, Luca, Tran, Huong Elena, Votta, Claudio, Larosa, Luigi, Minordi, Laura Maria, Maresca, Rossella, Pugliese, Daniela, Zocco, Maria Assunta, Ainora, Maria Elena, Lopetuso, Loris Riccardo, Papa, Alfredo, Armuzzi, Alessandro, Gasbarrini, Antonio, and Scaldaferri, Franco

Published
2023
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28. Si-based GeSn photodetectors towards mid-infrared imaging applications

Author

Tran, Huong, Pham, Thach, Margetis, Joe, Zhou, Yiyin, Dou, Wei, Grant, Perry C., Grant, Joshua M., Alkabi, Sattar, Sun, Greg, Soref, Richard A., Tolle, John, Zhang, Yong-Hang, Du, Wei, Li, Baohua, Mortazavi, Mansour, and Yu, Shui-Qing

Subjects
Physics - Applied Physics
Abstract

This paper reports a comprehensive study of Si-based GeSn mid-infrared photodetectors, which includes: 1) the demonstration of a set of photoconductors with Sn compositions ranging from 10.5% to 22.3%, showing the cut-off wavelength has been extended to 3.65 um. The measured maximum D* of 1.1x10^10 cmHz^(1/2)W(-1) is comparable to that of commercial extended-InGaAs detectors; 2) the development of surface passivation technique on photodiode based on in-depth analysis of dark current mechanism, effectively reducing the dark current. Moreover, mid-infrared images were obtained using GeSn photodetectors, showing the comparable image quality with that acquired by using commercial PbSe detectors., Comment: 25 pages, 8 figures

Published
2019

34. Brain atrophy pattern in patients with mild cognitive impairment: MRI study

Author

Calandrelli Rosalinda, Panfili Marco, Onofrj Valeria, Tran Huong Elena, Piludu Francesca, Guglielmi Valeria, Colosimo Cesare, and Pilato Fabio

Subjects
brain mri, regional atrophy patterns, visual rating scales, regional thickness and volume measures, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

We evaluated the accuracy of the quantitative and semiquantitative analysis in detecting regional atrophy patterns and differentiating mild cognitive impairment patients who remain stable (aMCI-S) from patients who develop Alzheimer’s disease (aMCI-AD) at clinical follow-up. Baseline magnetic resonance imaging was used for quantitative and semiquantitative analysis using visual rating scales. Visual rating scores were related to gray matter thicknesses or volume measures of some structures belonging to the same brain regions. Receiver operating characteristic (ROC) analysis was performed to assess measures’ accuracy in differentiating aMCI-S from aMCI-AD. Comparing aMCI-S and aMCI-AD patients, significant differences were found for specific rating scales, for cortical thickness belonging to the middle temporal lobe (MTL), anterior temporal (AT), and fronto-insular (FI) regions, for gray matter volumes belonging to MTL and AT regions. ROC curve analysis showed that middle temporal atrophy, AT, and FI visual scales showed better diagnostic accuracy than quantitative measures also when thickness measures were combined with hippocampal volumes. Semiquantitative evaluation, performed by trained observers, is a fast and reliable tool in differentiating, at the early stage of disease, aMCI patients that remain stable from those patients that may progress to AD since visual rating scales may be informative both about early hippocampal volume loss and cortical thickness reduction.

Published
2022
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40. Childhood encephalitis in the Greater Mekong region (the SouthEast Asia Encephalitis Project): a multicentre prospective study

Author

Buchy, Philippe, Bunnakea, Em, Cappelle, Julien, Channa, Mey, Chevalier, Veronique, Crabol, Yoann, de Lamballerie, Xavier, Dubot-Pérès, Audrey, Dussart, Philippe, Eloit, Marc, Gorman, Chris, Herrant, Magali, Hien, Nguyen, Hlaing, Chaw Su, Honnorat, Jérôme, Hung, Tran Thi Mai, Huong, Tran Thi Thu, Kyaw, Latt Latt, Lam, Nguyen Van, Laurent, Denis, Lecuit, Marc, Linn, Kyaw, Lortholary, Olivier, Mayxay, Mayfong, Min Aye, Aye Mya, Newton, Paul, Perot, Philippe, Phangmanixay, Sommanikhone, Phongsavath, Khounthavy, Phuc, Phan Huu, Pinto, Anne-Laurie, Piola, Patrice, Pommier, Jean-David, Rattanavong, Sayaphet, Rosset, Bruno, Santy, Ky, Sothy, Heng, Tarantola, Arnaud, Thuy, Nguyen Thi Thu, Tin, Htay Htay, Tin, Ommar Swe, Vongsouvath, Manivanh, An, Pham Nhat, Anh, Dang Duc, Bonnet, Pascal, Bun, Kimrong, Chommanam, Danoy, Davong, Viengmon, Debré, Patrice, Delfraissy, Jean-François, Devaux, Christian, Douangnouvong, Anousone, Duong, Veasna, Durand, Benoit, Eng, Chanreaksmey, Ferrant, Catherine, Fontenille, Didier, Hafner, Lukas, Hai, Le Thanh, Huong, Do Thu, Jouan, Marc, July, May, Lago, Magali, Moatti, Jean-Paul, Murgue, Bernadette, Oo, Khin Yi, Oum, MengHeng, Phakhounthong, Khansoudaphone, Pham, Anh Tuan, Quyen, Do, Seephonelee, Malee, Seguy, Maud, Sibounheunang, Bountoy, Sim, Kanarith, Tan, Luong Minh, Thair, Cho, Thein, Win, Thuy, Phung Bich, Tissot-Dupont, Hervé, Vongsouvath, Malavanh, Pommier, Jean David, Bleakley, Kevin, Tran, Huong Thi Thu, Nguyen, Lam Van, Aye, Aye Mya Min, Honnorat, Jerome, Pinto, Anne Laure, Tran, Thi Mai Hung, Pérot, Philippe, Phan, Phuc Huu, and Newton, Paul N

Published
2022
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46. Software compensation in Particle Flow reconstruction

Author

Tran, Huong Lan, Krüger, Katja, Sefkow, Felix, Green, Steven, Marshall, John, Thomson, Mark, and Simon, Frank

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The particle flow approach to calorimetry benefits from highly granular calorimeters and sophisticated software algorithms in order to reconstruct and identify individual particles in complex event topologies. The high spatial granularity, together with analogue energy information, can be further exploited in software compensation. In this approach, the local energy density is used to discriminate electromagnetic and purely hadronic sub-showers within hadron showers in the detector to improve the energy resolution for single particles by correcting for the intrinsic non-compensation of the calorimeter system. This improvement in the single particle energy resolution also results in a better overall jet energy resolution by improving the energy measurement of identified neutral hadrons and improvements in the pattern recognition stage by a more accurate matching of calorimeter energies to tracker measurements. This paper describes the software compensation technique and its implementation in particle flow reconstruction with the Pandora Particle Flow Algorithm (PandoraPFA). The impact of software compensation on the choice of optimal transverse granularity for the analogue hadronic calorimeter option of the International Large Detector (ILD) concept is also discussed., Comment: 13 pages, 13 figures, v2 with wording and figure improvements, published in EPJ C

Published
2017
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47. Development of a computer-based tool to obtain a family health history in Vietnam.

Author

Tieu, Linh Ba, Nguyen, Vinh Nhu, Tran, Phu Gia, Truong, Hieu Minh, Nguyen, Tuyet-Lan Thi, Huy Nguyen, Vu Quoc, Thi Ha, Thi Minh, Nguyen, Nguyen Huu, Lu, Y-Thanh, Tran, Huong-Thao Xuan, Nguyen, Quynh-Tho Thi, Nguyen, Quynh Nhat, Le, Toan Thanh, Truong, Viet Hoang, Bui, Minh Binh, Truong, Dinh-Kiet, Do, Thanh-Thuy Thi, Nguyen, Hoai-Nghia, Quan, Dieu Chan, and Tang, Hung-Sang

Abstract

Background: Family health history (FHH) is central to human genomic profiling construction; however, there is no protocol for documenting FHH in a pedigree format in Vietnam. Aim: A "Gia Su Suc Khoe" (GSSK) tool was developed to create a user-friendly interface for collecting FHH and offering diseases' risk assessment. Results: A tool was described (https://giasusuckhoe.vn/) with good feedback from genetic counselors and family-medicine doctors. Among 20 surveys, 100% of respondents noted that the report accurately reflected their FHH and were satisfied with the tool's display. About 74% of familial conditions were covered. Overall, all constructive feedback has been adapted into the updated version. Conclusion: Gia Su Suc Khoe has the potential to significantly improve healthcare delivery and outcomes in Vietnam. Article highlights Family health history (FHH) has been a primordial, non-expensive, and non-invasive risk stratification tool to inform prevention and screening strategies, especially for common genetic conditions. A new program developed in Vietnam namely Gia Su Suc Khoe (GSSK) not only utilizes a pedigree format to capture FHH but also performs risk assessments for various preventable and prevalent genetic disorders. GSSK is a self-administered, patient-centric and computerized program for obtaining Vietnamese FHH (https://giasusuckhoe.vn/), which receives positive feedback from genetic counselors and family-medicine doctors on its effectiveness, precision, and practical implications. GSSK becomes the pioneering program to help obtain FHH in Vietnam, which is believed to address several barriers, ensure high-quality information and leverage it for risk assessment. [ABSTRACT FROM AUTHOR]

Published
2024
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