Abstract: In this paper, a greedy initialization-based genetic algorithm is proposed for unit-norm tight frame sampling design in spherical near-field measurements. Since the optimization criterion ...

Personalized algorithms may quietly sabotage how people learn, nudging them into narrow tunnels of information even when they start with zero prior knowledge. In the study, participants using ...

We propose TraceRL, a trajectory-aware reinforcement learning method for diffusion language models, which demonstrates the best performance among RL approaches for DLMs. We also introduce a ...

The RL-FRB/US framework combines the Federal Reserve Board's macroeconomic model (FRB/US) with reinforcement learning techniques to optimize economic policy decisions. This integration, detailed in ...

Before diving into the details, let’s look at a high-level overview outlining vocabulary terms we’ll see come up and contrasting different methods. It would also be useful to revisit this section ...

Abstract: Inter-symbol interference (ISI) limits reliability in diffusion-based molecular communication (MC) channels. We propose RLIM, a family of run-length-limited (RLL) codes that form fixed-size ...