687 citations
- University of Southern CaliforniaUS6 papers
- National Institute of InformaticsJP4 papers
- KDDI (Japan)JP2 papers
- Osaka Institute of TechnologyJP2 papers
- Shibaura Institute of TechnologyJP2 papers
- Accenture (United States)US1 paper
- California Southern UniversityUS1 paper
- Carnegie Mellon UniversityUS1 paper
- Commonwealth Scientific and Industrial Research OrganisationAU1 paper
- Data61AU1 paper
- Doshisha UniversityJP1 paper
- Escuela Superior Politecnica del LitoralEC1 paper
38 papers
Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systems
Wen Ye, Muyan Weng, Chuizheng Meng +3
Understanding human mobility is critical for a wide range of urban applications, including traffic management, epidemic control, and urban planning. However, due to privacy concern…
bFaaaP: An Inclusive, Head-Angle Piano-Pedal Interaction that Quantitatively Reproduces a Pianist's Intended Pedalling -- Foot-Free, for Acoustic and Electronic Pianos
Tomoyuki Shishido, Masahiro Ootaki, Hiroyuki Narusawa
Expressive piano performance depends on the sustain (damper) pedal, operated by foot, excluding players who cannot readily use their feet: wheelchair users and others with lower-li…
Grover's algorithm for image edge detection
Pulak Ranjan Giri, Kazuhiro Saito
Grover's algorithm is one of the most significant quantum algorithms, enabling the search for a marked element in an unsorted database quadratically faster than classical search al…
Quantum hash function using discrete-time quantum walk on Hanoi network
Pulak Ranjan Giri
Quantum walk based hash functions have attracted a lot of attention in recent years because of its faster execution time and robust resistance against attacks compared to classical…
CUNEC: A Path Loss Model for Urban Cell-Free Massive MIMO Networks
Thomas Choi, Yuning Zhang, Issei Kanno +2
Accurate path loss (PL) modeling is essential for evaluating and optimizing cell-free massive MIMO systems, especially in dense urban environments where traditional models fail to…
Learning After Model Deployment
Derda Kaymak, Gyuhak Kim, Tomoya Kaichi +2
In classic supervised learning, once a model is deployed in an application, it is fixed. No updates will be made to it during the application. This is inappropriate for many dynami…