Zobrazeno 1 - 10
of 1 079
pro vyhledávání: '"Miller, Zachary A"'
Autor:
Zhou, Xuanru, Lian, Jiachen, Cho, Cheol Jun, Liu, Jingwen, Ye, Zongli, Zhang, Jinming, Morin, Brittany, Baquirin, David, Vonk, Jet, Ezzes, Zoe, Miller, Zachary, Tempini, Maria Luisa Gorno, Anumanchipalli, Gopala
Speech dysfluency modeling is a task to detect dysfluencies in speech, such as repetition, block, insertion, replacement, and deletion. Most recent advancements treat this problem as a time-based object detection problem. In this work, we revisit thi
Externí odkaz:
http://arxiv.org/abs/2409.13582
Autor:
Zhou, Xuanru, Cho, Cheol Jun, Sharma, Ayati, Morin, Brittany, Baquirin, David, Vonk, Jet, Ezzes, Zoe, Miller, Zachary, Tee, Boon Lead, Tempini, Maria Luisa Gorno, Lian, Jiachen, Anumanchipalli, Gopala
Current de-facto dysfluency modeling methods utilize template matching algorithms which are not generalizable to out-of-domain real-world dysfluencies across languages, and are not scalable with increasing amounts of training data. To handle these pr
Externí odkaz:
http://arxiv.org/abs/2409.09621
Autor:
Zhou, Xuanru, Kashyap, Anshul, Li, Steve, Sharma, Ayati, Morin, Brittany, Baquirin, David, Vonk, Jet, Ezzes, Zoe, Miller, Zachary, Tempini, Maria Luisa Gorno, Lian, Jiachen, Anumanchipalli, Gopala Krishna
Dysfluent speech detection is the bottleneck for disordered speech analysis and spoken language learning. Current state-of-the-art models are governed by rule-based systems which lack efficiency and robustness, and are sensitive to template design. I
Externí odkaz:
http://arxiv.org/abs/2408.15297
Species' interactions are shaped by their traits. Thus, we expect traits -- in particular, trait (dis)similarity -- to play a central role in determining whether a particular set of species coexists. Traits are, in turn, the outcome of an eco-evoluti
Externí odkaz:
http://arxiv.org/abs/2310.14392
An independent set in a graph $G$ is a set $S$ of pairwise non-adjacent vertices in $G$. A family $\mathcal{F}$ of independent sets in $G$ is called a $k$-independence covering family if for every independent set $I$ in $G$ of size at most $k$, there
Externí odkaz:
http://arxiv.org/abs/2308.15671
Autor:
Fristrup, Kurt1 (AUTHOR) Kurt.Fristrup@colostate.edu, Miller, Zachary D.2 (AUTHOR), Newton, Jennifer3 (AUTHOR), Buckley, Stephanie4 (AUTHOR), Cole, Hunter5 (AUTHOR), Linares, Carlos5 (AUTHOR), Donners, Maurice6 (AUTHOR), Taff, B. Derrick7 (AUTHOR), Beeco, J. Adam1 (AUTHOR), Barber, Jesse8 (AUTHOR), Newman, Peter9 (AUTHOR)
Publikováno v:
Scientific Reports. 9/18/2024, Vol. 14 Issue 1, p1-13. 13p.
Autor:
Miller, Zachary, Johnson, Kevin
Purpose: To investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D Pulmonary ultrashort echo time (UTE) acquisitions. Theory and Methods: A self-supervised eXtra Dimension MBDL
Externí odkaz:
http://arxiv.org/abs/2210.04436
Autor:
Abdi, Zeinab, Agosta, Federica, Ahmed, Samrah, Alcolea, Daniel, Allen, Isabel Elaine, Allinson, Kieren S.J., Apostolova, Liana G., Arighi, Andrea, Balasa, Mircea, Barkhof, Frederik, Best, John, Boon, Baayla D., Brandt, Katherine D., Brosch, Jared, Burrell, James, Butler, Christopher R., Calandri, Ismael, Caminiti, Silvia Paola, Canu, Elisa, Carrillo, Maria C., Caso, Francesca, Chapleau, Marianne, Chrem Mendez, Patricio, Chu, Min, Crutch, Sebastian, Cordato, Nicholas, Costa, Ana Sofia, Cui, Yue, Dickerson, Bradford, Dickson, Dennis W., Duara, Ranjan, Dubois, Bruno, Eldaief, Mark, Farlow, Martin, Fenoglio, Chiara, Filippi, Massimo, Fliessbach, Klaus, Formaglio, Maïté, Fortea, Juan, Fox, Nick, Foxe, David, Tilikete, Caroline Froment, Frosch, Matthew P., Fumagalli, Giorgio Giulio, Galasko, Douglas, Galimberti, Daniela, Garat, Oscar, Giardinieri, Giulia, Graff-Radford, Jonathan, Graff-Radford, Neill R., Grinberg, Lea, Groot, Colin, Hake, Ann Marie, Hansson, Oskar, Headley, Alison, Hernandez, Micaela, Hochberg, Daisy, Hodges, John R., Hof, Patrick R., Holton, Janice, Hromas, Gabrielle, Gala, Ignacio Illán, Irwin, David J., Jaunmuktane, Zane, Jing, Donglai, Josephs, Keith, Kagerer, Sonja M., Kasuga, Kensaku, Kong, Yu, Kövari, Enikö, Lacombe-Thibault, Mégane, Lleó, Alberto, Laforce, Robert, La Joie, Renaud, Lashley, Tammaryn, Leger, Gabriel, Levin, Netta, Levy, Richard, Liu, Yang, Liu, Li, Lladó Plarrumaní, Albert, Lucente, Diane E., Machulda, Mary M., Magnani, Giuseppe, Magnin, Eloi, Malpetti, Maura, Matthews, Brandy, McGinnis, Scott, Mendez, Mario F., Mesulam, Marsel, Migliaccio, Raffaella, Miklitz, Carolin, Miller, Zachary A., Montembeault, Maxime, Murray, Melissa E., Mundada, Nidhi, Nemes, Sara, Nestor, Peter J., Ocal, Dilek, Ossenkoppele, Rik, Paterson, Ross, Pelak, Victoria, Perani, Daniela, Phillips, Jeffrey, Piguet, Olivier, Pijnenburg, Yolande, Putcha, Deepti, Quimby, Megan, Rabinovici, Gil D., Reetz, Kathrin, Rein, Netaniel, Revesz, Tamas, Rezaii, Neguine, Rodriguez-Porcel, Federico, Rogalski, Emily, Rowe, James B., Ryan, Natalie, Sanchez-Valle, Raquel, Sacchi, Luca, Santos-Santos, Miguel Ángel, Schott, Jonathan M., Seeley, William, Sherman, Janet, Spina, Salvatore, Stomrud, Erik, Sullivan, A. Campbell, Tanner, Jeremy, Tideman, Pontus, Tokutake, Takayoshi, Tondo, Giacomo, Touroutoglou, Alexandra, Tousi, Babak, Vandenberghe, Rik, van der Flier, Wiesje, Walker, Jamie M., Weintraub, Sandra, Whitwell, Jennifer L., Wolk, David A., Wong, Bonnie, Wu, Liyong, Xie, Kexin, Yong, Keir, Apostolova, Liana, Boon, Baayla D C, Grinberg, Lea T, Irwin, David J, Josephs, Keith A, Mendez, Mario F, Mendez, Patricio Chrem, Miller, Zachary A, Murray, Melissa E, Nemes, Sára, Schott, Jonathan M, Sullivan, A Campbell, Walker, Jamie, Whitwell, Jennifer L, Wolk, David A, Rabinovici, Gil D *
Publikováno v:
In The Lancet Neurology February 2024 23(2):168-177
Purpose: To improve upon Extreme MRI, a recently proposed method by Ong Et al. for reconstructing high spatiotemporal resolution, 3D non-Cartesian acquisitions by incorporating motion compensation into these reconstructions using an approach termed M
Externí odkaz:
http://arxiv.org/abs/2205.00131
Objective: Model based deep learning (MBDL) has been challenging to apply to the reconstruction of 3D non-Cartesian MRI acquisitions due to extreme GPU memory demand (>250 GB using traditional backpropagation) primarily because the entire volume is n
Externí odkaz:
http://arxiv.org/abs/2204.13862