James DeFilippis
Rutgers University
38 Papers
212 Citations
James DeFilippis is an academic researcher from Rutgers University. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 11, co-authored 38 publications. Previous affiliations of James DeFilippis include University of Science and Technology, Liaoning.
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Papers
Phase transition and plastic deformation mechanisms induced by self-rotating grinding of GaN single crystals
TL;DR: In this paper, the deformation and removal mechanisms of gallium nitride (GaN) single crystals involved in the ultra-precision machining process are not well revealed and few investigations on the grinding of GaN crystals have been reported.
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Phase transition and plastic deformation mechanisms induced by self-rotating grinding of GaN single crystals
James DeFilippis
- 01 Jan 2022
TL;DR: In this paper , the deformation and removal mechanisms of gallium nitride (GaN) single crystals involved in the ultra-precision machining process are not well revealed and few investigations on the grinding of GaN crystals have been reported.
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Anisotropy dependence of material removal and deformation mechanisms during nanoscratch of gallium nitride single crystals on (0001) plane
TL;DR: In this paper, the anisotropy dependence of material removal and deformation behaviors was investigated systematically, and the results showed that crack-free plastic deformation of GaN crystals could be acquired along different zone axes, which was dominated by phase transition, polycrystalline nanocrystals, amorphous transition, as well as close-to-atomic scale damages including stacking faults, dislocations and lattice distortions.
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GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer
TL;DR: In this article , a noble publicly available Gastric Histopathology Sub-size Image Database (GasHisSDB) is published to identify classifiers' performance, which includes two types of data: normal and abnormal, with a total of 245,196 tissue case images.
Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers.
Wanli Liu,James DeFilippis,Chen Li,Mamunur Rahaman,Tao Jiang,Hongzan Sun,Xiangchen Wu,Weiming Hu,Haoyuan Chen,Changhao Sun,Changhao Sun,Yu-Dong Yao,Marcin Grzegorzek +12 more
TL;DR: Wang et al. as mentioned in this paper conducted a series of comparative experiments to determine a reasonable interpretation, and concluded that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images.
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