Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized...Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.展开更多
Being able to tailor the composition at the voxel-size resolution,additive manufacturing of alloys calls for effective models to explore the vast and complex design space.We present AlloyGPT,a generative alloy-specifi...Being able to tailor the composition at the voxel-size resolution,additive manufacturing of alloys calls for effective models to explore the vast and complex design space.We present AlloyGPT,a generative alloy-specific language model that concurrently performs forward property prediction and inverse alloy design.By converting physics-informed alloy data into structured textual representations,our model learns to capture intricate composition–structure–property relationships.It demonstrates high predictive accuracy across multiple phases and properties(R^(2)=0.86-0.99)and robust generalization to unseen compositions.In inverse design tasks,it can generate diverse alloy candidates that meet specified property targets,showcasing its versatility.Comprehensive attention patterns and reasoning paths are observed within the model,suggesting promising clues for underlying alloy physics.By synergizing accuracy,diversity and robustness in prediction and design tasks,AlloyGPTis expected to accelerate knowledge integration and material design for uniform or gradient structural alloys manufactured by traditional and additive manufacturing.展开更多
基金supported by the National Natural Science Foundation of China,No.31070758,31271060the Natural Science Foundation of Chongqing in China,No.cstc2013jcyj A10085
文摘Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.
基金support from Naval Nuclear Laboratory(NNL)award No.1047622,This research was conducted using the Tartan Research Advanced Computing Environment(TRACE).The authors would like to gratefully acknowledge the College of Engineering at Carnegie Mellon University for making this shared high-performance computing resource available to its community.
文摘Being able to tailor the composition at the voxel-size resolution,additive manufacturing of alloys calls for effective models to explore the vast and complex design space.We present AlloyGPT,a generative alloy-specific language model that concurrently performs forward property prediction and inverse alloy design.By converting physics-informed alloy data into structured textual representations,our model learns to capture intricate composition–structure–property relationships.It demonstrates high predictive accuracy across multiple phases and properties(R^(2)=0.86-0.99)and robust generalization to unseen compositions.In inverse design tasks,it can generate diverse alloy candidates that meet specified property targets,showcasing its versatility.Comprehensive attention patterns and reasoning paths are observed within the model,suggesting promising clues for underlying alloy physics.By synergizing accuracy,diversity and robustness in prediction and design tasks,AlloyGPTis expected to accelerate knowledge integration and material design for uniform or gradient structural alloys manufactured by traditional and additive manufacturing.
