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DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
[article]
2021
arXiv
pre-print
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learning frameworks for geometry, reflectance and lighting prediction from a single image without requiring any ground-truth. ...
Many previous learning-based approaches for inverse graphics adopt rasterization-based renderers and assume naive lighting and material models, which often fail to account for non-Lambertian, specular ...
Conclusion We presented DIB-R++, a hybrid differentiable renderer that can effectively disentangle material and lighting. ...
arXiv:2111.00140v1
fatcat:bdlbtkktm5hhha33mcnxv7bbh4
The involvement of central L-type calcium channel subtypes CaV1.2 and CaV1.3 in alcohol dependence and comorbid mental disorders
[article]
2016
Preserved Fulltext
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While for some people it is not a problem to control their intake and use alcohol recreationally, others escalate their drinking until it becomes compulsive. ...
One possible target are the L-type calcium channels (LTCCs), which are influenced by both alcohol and nicotine, and have also been implicated in the risk to develop schizophrenia and depression. ...
The Diagnostic Instrument for Brain Studies -Revised (DIBS-R), which is consistent with the criteria of the Diagnostic and Statistical Manual for Mental Disorders, 4th edition (DSM-IV) (American Psychiatric ...
doi:10.11588/heidok.00021487
fatcat:5ujdhmv6rrcopm7hnkft624f7e
Systems Metabolic Engineering of Microbial Cell Factories for the Synthesis of Value-added Chemicals
2013
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The authors also thank Bing Wu, Xueyang Feng, Peter Colletti and Yogesh Goyal for helping with data collection.
Acknowledgement We thank Prof. ...
We also thank Dianyi Liu, Kanimozhi, and Zach Hembree for their help with experiments, and Sandra Matteucci from the WUSTL Engineering Communication Center, for her close reading of the manuscript. ...
I would also like to thank my co-advisor Prof. Himadri Pakrasi for providing me with a wonderful opportunity to work and learn from his lab. ...
doi:10.7936/k7pz56w4
fatcat:qheccgzoyvb6xpdvs2g7ehzzd4