191 citations
- Duke UniversityUS17 papers
- United States ArmyUS17 papers
- University of North TexasUS16 papers
- North Carolina State UniversityUS14 papers
- University of PisaIT8 papers
- TriangleUS4 papers
- Wrocław University of Science and TechnologyPL4 papers
- National Academy of Sciences of UkraineUA3 papers
- University of North Carolina at Chapel HillUS3 papers
- DEVCOM Army Research LaboratoryUS2 papers
- École Polytechnique Fédérale de LausanneCH2 papers
- Institute for Computational Linguistics “A. Zampolli”IT2 papers
50 papers
Forum on immune digital twins: a meeting report
Reinhard Laubenbacher, Fred Adler, Gary An +16
Medical digital twins are computational models of human biology relevant to a given medical condition, which can be tailored to an individual patient, thereby predicting the course…
Long-lived modulation of plasmonic absorption by ballistic thermal injection
John A. Tomko, Evan L. Runnerstrom, Yi-Siang Wang +9
Energy and charge transfer across metal-semiconductor interfaces are the fundamental driving forces for a broad range of applications, such as computing, energy harvesting, and pho…
RED: A ReRAM-based Deconvolution Accelerator
Zichen Fan, Ziru Li, Bing Li +3
Deconvolution has been widespread in neural networks. For example, it is essential for performing unsupervised learning in generative adversarial networks or constructing fully con…
Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks
Siddharth Roheda, Benjamin S. Riggan, Hamid Krim +1
In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i.e. transferring) knowledge from sensor data and enhancing low-resolution targ…
Complexity Matching and Requisite Variety
Korosh Mahmoodi, Bruce J. West, Paolo Grigolini
Complexity matching characterizes the role of information in interactions between systems and can be traced back to the 1957 Introduction to Cybernetics by Ross Ashby. We argue tha…
Structured Analysis Dictionary Learning for Image Classification
Wen Tang, Ashkan Panahi, Hamid Krim +1
We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning. To achieve more…