Zobrazeno 1 - 10
of 179 440
pro vyhledávání: '"Kahn"'
Publikováno v:
Drug Design, Development and Therapy, Vol Volume 17, Pp 1323-1327 (2023)
Sarah Balboul,1 Julia Kahn,1 Alexis Tracy,2 Anjelica Peacock,3 Abigail Cline2– 4 1School of Medicine, New York Medical College, Valhalla, NY, USA; 2Department of Dermatology, New York Medical College, Valhalla, NY, USA; 3Department of Dermatology,
Externí odkaz:
https://doaj.org/article/c779a387fa9e4b0e9852059c569c4b17
Publikováno v:
BMC Genomics, Vol 24, Iss 1, Pp 1-4 (2023)
Externí odkaz:
https://doaj.org/article/18f847f63a9a422e91d13312856363ff
Autor:
Farshad Farshidfar, Kahn Rhrissorrakrai, Chaya Levovitz, Cong Peng, James Knight, Antonella Bacchiocchi, Juan Su, Mingzhu Yin, Mario Sznol, Stephan Ariyan, James Clune, Kelly Olino, Laxmi Parida, Joerg Nikolaus, Meiling Zhang, Shuang Zhao, Yan Wang, Gang Huang, Miaojian Wan, Xianan Li, Jian Cao, Qin Yan, Xiang Chen, Aaron M. Newman, Ruth Halaban
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-16 (2022)
Despite acral melanoma being the most common melanoma subtype in non-White individuals, its molecular drivers remain unknown. Here, the authors integrate genomic and clinical data from 104 patients and identify late-arising focal amplifications of ch
Externí odkaz:
https://doaj.org/article/b66dfca16a5a4cbdb29e51d65dceaf25
Publikováno v:
BMC Genomics, Vol 22, Iss S5, Pp 1-13 (2021)
Abstract Background All diseases containing genetic material undergo genetic evolution and give rise to heterogeneity including cancer and infection. Although these illnesses are biologically very different, the ability for phylogenetic retrodiction
Externí odkaz:
https://doaj.org/article/139dda9521494055830a756d94608682
Apolipoprotein C3 and circulating mediators of preadipocyte proliferation in states of lipodystrophy
Autor:
Brandao Bruna Brasil, Sakaguchi Masaji, Batista, Thiago Martins, Hu Jiang, Nie Song, Schepmoes Athena A, BonDurant Lucas, Moreau François, Qian Wei-Jun, Kulkarni N. Rohit, Kahn, C. Ronald
Publikováno v:
Molecular Metabolism, Vol 64, Iss , Pp 101572- (2022)
Adipogenesis is a complex process controlled by intrinsic and extrinsic factors that regulate preadipocyte proliferation, adipogenic capacity and maturation of metabolic function. Here we show that insulin and IGF-1 receptors are essential for mature
Externí odkaz:
https://doaj.org/article/485b206b95a246cd93c323de93b87a1b
Autor:
Kahn, Bruno
We show that the continuous \'etale cohomology groups $H^n_{\mathrm{cont}}(X,\mathbf{Q}_l(n))$ of smooth varieties $X$ over a finite field $k$ are spanned as $\mathbf{Q}_l$-vector spaces by the $n$-th Milnor $K$-sheaf locally for the Zariski topology
Externí odkaz:
http://arxiv.org/abs/2409.10248
Typical multi-mode fibers exhibit strong intra-group mode coupling and weak inter-group mode coupling. Mode scramblers can be inserted at periodic intervals to enhance inter-group coupling. The deterministic mode coupling of the mode scramblers, in c
Externí odkaz:
http://arxiv.org/abs/2409.06908
Social scientists quickly adopted large language models due to their ability to annotate documents without supervised training, an ability known as zero-shot learning. However, due to their compute demands, cost, and often proprietary nature, these m
Externí odkaz:
http://arxiv.org/abs/2409.02078
Autor:
Khani, Nikhil, Yang, Shuo, Nath, Aniruddh, Liu, Yang, Abbo, Pendo, Wei, Li, Andrews, Shawn, Kula, Maciej, Kahn, Jarrod, Zhao, Zhe, Hong, Lichan, Chi, Ed
Knowledge Distillation (KD) is a powerful approach for compressing a large model into a smaller, more efficient model, particularly beneficial for latency-sensitive applications like recommender systems. However, current KD research predominantly foc
Externí odkaz:
http://arxiv.org/abs/2408.14678
Autor:
Zhou, Chunting, Yu, Lili, Babu, Arun, Tirumala, Kushal, Yasunaga, Michihiro, Shamis, Leonid, Kahn, Jacob, Ma, Xuezhe, Zettlemoyer, Luke, Levy, Omer
We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality s
Externí odkaz:
http://arxiv.org/abs/2408.11039