Results 221 to 230 of about 1,278,153 (338)

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth

Advanced Theory and Simulations, EarlyView.
A data‐driven framework combining Computational Fluid Dynamics (CFD) simulations and machine learning is proposed to model and optimize Czochralski crystal growth. Using different transfer learning strategies (Warm Start, Merged Training, and Hyperparameter Transfer) the study demonstrates improved predictions for Ge and GaAs growth from Si‐trained ...
Milena Petkovic, Natasha Dropka, Xia Tang, Janina Zittel   +3 more
wiley   +1 more source

Integrating gene expression data via weighted multiple kernel ridge regression improved accuracy of genomic prediction. [PDF]

Genet Sel Evol
Wang X, Si J, Wang Y, Fang L, Zhang Z, Zhang Y.   +5 more
europepmc   +1 more source

Investigations of the Direction of Expansion of Humified Rice Kernels in Relation to Their Cracking

, 1932
Mantarō Kondō, Tamotsu Okamura
openalex   +2 more sources

TVB C++: A Fast and Flexible Back‐End for The Virtual Brain

Advanced Science, EarlyView.
TVB C++ is a streamlined and fast C++ Back‐End for The Virtual Brain (TVB), designed to make it as flexible as TVB, and FAST. Another pillar is to be fully compatible with TVB so easy bindings can be created from Python. Users can easily configure TVB C++ to execute the same code but with enhanced performance and parallelism.
Ignacio Martín, Gorka Zamora‐López, Jan Fousek, Michael Schirner, Petra Ritter, Viktor Jirsa, Gustavo Deco, Gustavo Patow   +7 more
wiley   +1 more source

Three-dimensional noise characteristics of clinical photon counting detector CT. [PDF]

Med Phys
Monsivais H, Liu X, Dong F, Jacobsen MC, Rong J, Jensen CT, Li K.   +6 more
europepmc   +1 more source

On the Phenomenon of Fafing of Methylene Blue by Extracts of Unhulled Rice Kernels

, 1934
Hakuai UYEDA
openalex   +2 more sources

Remarks on the kernel of a matrix semigroup [PDF]

, 1965
W. Edwin Clark
openalex   +1 more source

CRCFound: A Colorectal Cancer CT Image Foundation Model Based on Self‐Supervised Learning

Advanced Science, EarlyView.
CRCFound is a self‐supervised learning‐based CT image foundation model for colorectal cancer (CRC). Pretrained on 5137 unlabeled CRC CT images, it learns universal feature representations, enabling efficient adaptation to various clinical tasks. The model demonstrates outstanding performance and generalization across multiple diagnostic and prognosis ...
Jing Yang, Du Cai, Junwei Liu, Zhenfeng Zhuang, Yibin Zhao, Feng‐ao Wang, Chenghang Li, Chuling Hu, Baowen Gai, Yiping Chen, Yixue Li, Liansheng Wang, Feng Gao, Xiaojian Wu   +13 more
wiley   +1 more source

Maize Kernel Batch Counting System Based on YOLOv8-ByteTrack. [PDF]

Sensors (Basel)
Li R, Liu Q, Wang M, Su Y, Li C, Ou M, Liu L.   +6 more
europepmc   +1 more source

On generalized kernels

Semigroup Forum, 1976
openaire   +3 more sources