More Like this

1. Scalar2Vec: Translating Scalar Fields to Vector Fields via Deep Learning

   https://doi.org/10.1109/PacificVis53943.2022.00012  

   Gu, Pengfei; Han, Jun; Chen, Danny Z.; Wang, Chaoli (April 2022, Proceedings of IEEE Pacific Visualization Symposium)
2. On the Frobenius fields of abelian varieties over number fields

   https://doi.org/10.2140/ant.2026.20.577  

   Burungale, Ashay A; Hida, Haruzo; Lai, Shilin (March 2026, Algebra & Number Theory)
3. Endomorphisms of power series fields and residue fields of Fargues-Fontaine curves

   https://doi.org/10.1090/proc/13818  

   Kedlaya, Kiran S.; Temkin, Michael (February 2018, Proceedings of the American Mathematical Society)
4. Cohomology groups of Fermat curves via ray class fields of cyclotomic fields

   https://doi.org/10.1016/j.jalgebra.2020.02.030  

   Davis, Rachel; Pries, Rachel (July 2020, Journal of Algebra)

   null (Ed.)
5. Event fields: Capturing light fields at high speed, resolution, and dynamic range

   https://doi.org/10.1109/CVPR52734.2025.02506  

   Qu, Ziyuan; Zou, Zihao; Boominathan, Vivek; Chakravarthula, Praneeth; Pediredla, Adithya (June 2025, IEEE)

   Event cameras, which feature pixels that independently respond to changes in brightness, are becoming increasingly popular in high- speed applications due to their lower latency, reduced bandwidth requirements, and enhanced dynamic range compared to traditional frame- based cameras. Numerous imaging and vision techniques have leveraged event cameras for high- speed scene understanding by capturing high- framerate, high- dynamic range videos, primarily utilizing the temporal advantages inherent to event cameras. Additionally, imaging and vision techniques have utilized the light field—a complementary dimension to temporal information—for enhanced scene understanding.In this work, we propose "Event Fields", a new approach that utilizes innovative optical designs for event cameras to capture light fields at high speed. We develop the underlying mathematical framework for Event Fields and introduce two foundational frameworks to capture them practically: spatial multiplexing to capture temporal derivatives and temporal multiplexing to capture angular derivatives. To realize these, we design two complementary optical setups— one using a kaleidoscope for spatial multiplexing and another using a galvanometer for temporal multiplexing. We evaluate the performance of both designs using a custom-built simulator and real hardware prototypes, showcasing their distinct benefits. Our event fields unlock the full advantages of typical light fields—like post- capture refocusing and depth estimation—now supercharged for high- speed and high- dynamic range scenes. This novel light- sensing paradigm opens doors to new applications in photography, robotics, and AR/VR, and presents fresh challenges in rendering and machine learning. 

   more » « less