3.7k citations
- University of California San DiegoUS25 papers
- The University of Texas at AustinUS14 papers
- Qualcomm (United Kingdom)GB11 papers
- University of California, IrvineUS10 papers
- University of California, Los AngelesUS10 papers
- École Polytechnique Fédérale de LausanneCH9 papers
- Market MattersUS9 papers
- Princeton UniversityUS9 papers
- University of California, BerkeleyUS9 papers
- University of TorontoCA9 papers
- Texas A&M UniversityUS8 papers
- University of Illinois Urbana-ChampaignUS8 papers
223 papers
A Comprehensive Review of Generative Physical Artificial Intelligence
Satyam Gaba, Krutiksinh Rana, Siva Sai +2
The integration of large-scale foundation models with physical embodiments has led to significant advancements in robotics known as Generative Physical Artificial Intelligence (GPA…
Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning
Jared Levy, Aarti Lalwani, Elijah Wyckoff +4
Back pain is a pervasive issue affecting a significant portion of the population, often worsened by certain movements of the lower back. Assessing these movements is important for…
Position Dependent Prediction Combination For Intra-Frame Video Coding
Amir Said, Xin Zhao, Marta Karczewicz +2
Intra-frame prediction in the High Efficiency Video Coding (HEVC) standard can be empirically improved by applying sets of recursive two-dimensional filters to the predicted values…
Low-Complexity Transform Adjustments For Video Coding
Amir Said, Hilmi E. Egilmez, Yung-Hsuan Chao
Recent video codecs with multiple separable transforms can achieve significant coding gains using asymmetric trigonometric transforms (DCTs and DSTs), because they can exploit dive…
Highly Efficient Non-Separable Transforms for Next Generation Video Coding
Amir Said, Xin Zhao, Marta Karczewicz +3
For the last few decades, the application of signal-adaptive transform coding to video compression has been stymied by the large computational complexity of matrix-based solutions.…
Alternative Framework to Quantize Fermionic Fields
Jianhao M. Yang
A variational framework is developed here to quantize fermionic fields based on the extended stationary action principle. From the first principle, we successfully derive the well-…