Microsoft researchers have developed On-Policy Context Distillation (OPCD), a training method that permanently embeds ...

Low-rank data analysis has emerged as a powerful paradigm across applied mathematics, statistics, and data science. With the rapid growth of modern datasets in size, dimensionality, and complexity, ...

Optimization problems often involve situations in which the user's goal is to minimize and/or maximize not a single objective function, but several, usually conflicting, functions simultaneously. Such ...

A total of 114 clinical medicine students who had internships in our hospital from January 2021 and December 2023 selected as the research participants. These students were randomly divided into a PBL ...

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...

Abstract: Solving discrete optimization problems is one of the most promising use cases on quantum computers. The libraries quark and quapps provide an easily usable, modular workflow to encode ...

When the pandemic upended the world of higher education, Robin Pugh, a professor at City College of San Francisco, began to see one puzzling problem in her online courses: Not everyone was a real ...

PALO ALTO, Calif. & CALGARY, Alberta--(BUSINESS WIRE)--D-Wave Quantum Inc. (NYSE: QBTS) (“D-Wave”), a leader in quantum computing systems, software, and services and the world’s first commercial ...

Abstract: A wide range of real applications can be modelled as the multiobjective traveling salesman problem (MOTSP), one of typical combinatorial optimization problems. Meta-heuristics can be used to ...

1 College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. 2 Shenyang Aircraft Design Institute, AVIC, Shenyang, China. The paper establishes a ...