Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Jason Dong"'
Autor:
Maddux Sy, Jason Dong, Sophia Wang, Simon Wang, Bill DeCesare, Sean Fleuriel, Todd Clark, Saminda Dharmarathna, Kesheng Feng
Publikováno v:
2021 16th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT).
Autor:
Bingwei Lu, Michael Snyder, Jason Dong, Yu Li, Zhihao Wu, Ishaq Tantray, Songjie Chen, Hannes Vogel, Shuangxi Li, Steven E. Glynn
Publikováno v:
Proc Natl Acad Sci U S A
Maintaining the fidelity of nascent peptide chain (NP) synthesis is essential for proteome integrity and cellular health. Ribosome-associated quality control (RQC) serves to resolve stalled translation, during which untemplated Ala/Thr residues are a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6025b9a9064defc5704ab17e87fd0647
https://europepmc.org/articles/PMC7547246/
https://europepmc.org/articles/PMC7547246/
Autor:
Shouvik Chatterjee, Felipe Crasto de Lima, John A. Logan, Yuan Fang, Hadass Inbar, Aranya Goswami, Connor Dempsey, Jason Dong, Shoaib Khalid, Tobias Brown-Heft, Yu-Hao Chang, Taozhi Guo, Daniel J. Pennachio, Nathaniel Wilson, Shalinee Chikara, Alexey Suslov, Alexei V. Fedorov, Dan Read, Jennifer Cano, Anderson Janotti, Christopher J. Palmstrøm
Publikováno v:
Physical Review Materials, vol 5, iss 12
Topological materials often exhibit remarkably linear, non-saturating magnetoresistance (LMR), which is both of scientific and technological importance. However, the role of topologically non-trivial states in the emergence of such a behaviour has el
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2faec490ae011de33858f58643764972
Publikováno v:
Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data.
The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. This paper borrows the idea of Super Characters method and tw
Publikováno v:
ICME Workshops
Computer Vision and Natural Language Processing (NLP) applications are becoming available at edge devices and mobile platforms with the mass production of low-power and high-performance AI chips. SPR2801s is the CNN Domain Specific Accelerator (CNN-D
Publikováno v:
CVPR Workshops
Tabular data is the most commonly used form of data in industry. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. DNN models using categorical embeddi
Autor:
Jason Dong, Ed Lowenhar, Hossein Saboonchi, Lu Zhang, Didem Ozevin, Obdulia Ley, Valery F. Godinez-Azcuaga
Publikováno v:
Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XIII.
Autor:
Georg Auburger, Zoe H. Davis, Ishaq Tantray, Michael Snyder, Xiaolin Bi, Yu Li, Zhihao Wu, Songjie Chen, Suzana Gispert, Junghyun Lim, Bingwei Lu, Cole S. Sitron, Jason Dong, Onn Brandman
Publikováno v:
Mol Cell
SUMMARYMitochondrial dysfunction and proteostasis failure frequently coexist as hallmarks of neurodegenerative disease. How these pathologies are related is not well understood. Here we describe a phenomenon termed MISTERMINATE (mitochondrial stress-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea27269405defe67e40760913d2763f7
Publikováno v:
CVPR Workshops
Computer vision performances have been significantly improved in recent years by Convolutional Neural Networks(CNN). Currently, applications using CNN algorithms are deployed mainly on general purpose hardwares, such as CPUs, GPUs or FPGAs. However,
Publikováno v:
WASSA@EMNLP
We propose a method named Super Characters for sentiment classification. This method converts the sentiment classification problem into image classification problem by projecting texts into images and then applying CNN models for classification. Text
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5be1319dad37b99cf820b49a88888cf5