A Monotonicity Result for the Range of a Perturbed Random Walk

We consider a discrete time simple symmetric random walk on Z^d, d>=1, where the path of the walk is perturbed by inserting deterministic jumps. We show that for any time n and any deterministic jumps that we insert, the expected number of sites visited by the perturbed random walk up to time n is always larger than or equal to that for the unperturbed walk. This intriguing problem arises from the study of a particle among a Poisson system of moving traps with sub-diffusive trap motion. In particular, our result implies a variant of the Pascal principle, which asserts that among all deterministic trajectories the particle can follow, the constant trajectory maximizes the particle's survival probability up to any time t>0.Comment: 10 pages, 1 figure. To appear in Journal of Theoretical Probabilit

Guest editorial for the special issue on software-defined radio transceivers and circuits for 5G wireless communications

Yichuang Sun, Baoyong Chi, and Heng Zhang, Guest Editorial for the Special Issue on Software-Defined Radio Transceivers and Circuits for 5G Wireless Communications, published in IEEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 63 (1): 1-3, January 2016, doi: https://doi.org/10.1109/TCSII.2015.2506979.Peer reviewedFinal Accepted Versio

How to Fine-Tune BERT for Text Classification?

Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets