Search Results for author: M. Li

Found 5 papers, 0 papers with code

Prepare Non-classical Collective Spin State by Reinforcement Learning

no code implementations29 Jan 2024 X. L. Zhao, Y. M. Zhao, M. Li, T. T. Li, Q. Liu, S. Guo, X. X. Yi

It is exemplified by the application to prepare spin squeezed state for an open collective spin model where a linear control term is designed to govern the dynamics.

reinforcement-learning

Joint Waveform and Filter Designs for STAP-SLP-based MIMO-DFRC Systems

no code implementations16 Dec 2021 R. Liu, M. Li, Q. Liu, A. L. Swindlehurst

Dual-function radar-communication (DFRC), which can simultaneously perform both radar and communication functionalities using the same hardware platform, spectral resource and transmit waveform, is a promising technique for realizing integrated sensing and communication (ISAC).

Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures

no code implementations21 Oct 2020 H. Bai, L. Tan, K. Xiong, M. Li, J. Lin

In this paper, we demonstrate under a Bayesian framework that distance between primitive statistics such as the mean of word embeddings are fundamentally flawed for capturing sentence-level semantic similarity.

Semantic Similarity Semantic Textual Similarity +3

Accurate and efficient Simulation of very high-dimensional Neural Mass Models with distributed-delay Connectome Tensors

no code implementations16 Sep 2020 A. González-Mitjans, D. Paz-Linares, A. Areces-Gonzalez, M. Li, Y. Wang, ML. Bringas-Vega, P. A Valdés-Sosa

Semi-analytical integration of the RDE is done with the Local Linearization scheme for each neural mass model, which is the only scheme guaranteeing dynamical fidelity to the original continuous-time nonlinear dynamic.

Computational Efficiency Electroencephalogram (EEG) +1

Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems

no code implementations26 Dec 2019 R. Liu, M. Li, Q. Liu, A. L. Swindlehurst

In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems.

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