11 citations
- Fujitsu (Japan)JP12 papers
- The University of TokyoJP5 papers
- Fujitsu (United States)US4 papers
- University of Illinois Urbana-ChampaignUS2 papers
- Ames Research CenterUS1 paper
- Arizona State UniversityUS1 paper
- Barcelona Supercomputing CenterES1 paper
- Carnegie Mellon UniversityUS1 paper
- Computing CenterRU1 paper
- Delft University of TechnologyNL1 paper
- Fujitsu (United Kingdom)GB1 paper
- Hokkaido UniversityJP1 paper
6 papers · 1 filter
User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios
Xiaoyuan Wu, Roshni Kaushik, Wenkai Li +2
Large language models (LLMs) are rapidly being adopted for tasks like drafting emails, summarizing meetings, and answering health questions. In these settings, users may need to sh…
Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models
Ahmadreza Saboor Yaraghi, Golnaz Gharachorlu, Sakina Fatima +3
Fault localization (FL) is a critical step in debugging, which typically relies on repeated executions to pinpoint faulty code regions. However, repeated executions can be impracti…
Guidelines for External Disturbance Factors in the Use of OCR in Real-World Environments
Kenji Iwata, Eiki Ishidera, Toshifumi Yamaai +6
The performance of OCR has improved with the evolution of AI technology. As OCR continues to broaden its range of applications, the increased likelihood of interference introduced…
Molecular Quantum Transformer
Yuichi Kamata, Quoc Hoan Tran, Yasuhiro Endo +1
The Transformer model, renowned for its powerful attention mechanism, has achieved state-of-the-art performance in various artificial intelligence tasks but faces challenges such a…
Josephson traveling-wave parametric amplifier based on low-intrinsic-loss coplanar lumped-element waveguide
C. W. Sandbo Chang, Arjan F. Van Loo, Chih-Chiao Hung +4
We present a Josephson traveling-wave parametric amplifier (JTWPA) based on a low-loss coplanar lumped-element waveguide architecture. By employing open-stub capacitors and Manhatt…
Contextual Quantum Neural Networks for Stock Price Prediction
Sharan Mourya, Hannes Leipold, Bibhas Adhikari
In this paper, we apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network. Our approach captures recent trends…