arXiv subjects

M. Yin

Publications and source records attributed to M. Yin.

5 recordsLinked to original sources

arXiv2026

Using Legacy Polysomnography Data to Train a Radar System to Quantify Sleep in Older Adults and People living with Dementia

Objective: Ultra-wideband radar technology offers a promising solution for unobtrusive and cost-effective in-home sleep monitoring. However, the limited availability of radar sleep data poses challenges in building robust models that generalize across diverse cohorts and environments. This study proposes a novel deep transfer learning framework to enhance sleep stage classification using radar data. Methods: An end-to-end neural network was developed to classify sleep stages based on nocturnal respiratory and motion signals. The network was trained using a combination of large-scale polysomnography (PSG) datasets and radar data. A domain adaptation approach employing adversarial learning was utilized to bridge the knowledge gap between PSG and radar signals. Validation was performed on a radar dataset of 47 older adults (mean age: 71.2), including 18 participants with prodromal or mild Alzheimer disease. Results: The proposed network structure achieves an accuracy of 79.5% with a Kappa value of 0.65 when classifying wakefulness, rapid eye movement, light sleep and deep sleep. Experimental results confirm that our deep transfer learning approach significantly enhances automatic sleep staging performance in the target domain. Conclusion: This method effectively addresses challenges associated with data variability and limited sample size, substantially improving the reliability of automatic sleep staging models, especially in contexts where radar data is limited. Significance: The findings underscore the viability of UWB radar as a nonintrusive, forward-looking sleep assessment tool that could significantly benefit care for older people and people with neurodegenerative disorders.

cs.LG↗

arXiv2025

Topology and Martensitic Phase Transformations

Triply periodic minimal surfaces (TPMS) are discovered to conform to surfaces of given charge density distributions embedded in crystals [Z. Kristallogr. \textbf{170}, 138 (1985)]. Based on our previous work [Phys. Rev. Mater. \textbf{9}, 073802 (2025)], we discovered that crystals can have surfaces of a given charge density converging to TPMS. We also discovered that end states connected by a martensitic phase transformation should have their corresponding TPMS being topologically equivalent. In this work, we gave an explanation for the topological continuity of a martensitic phase transformation and studied how TPMS indicate whether a non-magnetic crystal can undergo a martensitic phase transformation or not.

cond-mat.mtrl-sci↗

arXiv2022

Source-Free Domain Adaptation for Question Answering with Masked Self-training

Most previous unsupervised domain adaptation (UDA) methods for question answering(QA) require access to source domain data while fine-tuning the model for the target domain. Source domain data may, however, contain sensitive information and may be restricted. In this study, we investigate a more challenging setting, source-free UDA, in which we have only the pretrained source model and target domain data, without access to source domain data. We propose a novel self-training approach to QA models that integrates a unique mask module for domain adaptation. The mask is auto-adjusted to extract key domain knowledge while trained on the source domain. To maintain previously learned domain knowledge, certain mask weights are frozen during adaptation, while other weights are adjusted to mitigate domain shifts with pseudo-labeled samples generated in the target domain. %As part of the self-training process, we generate pseudo-labeled samples in the target domain based on models trained in the source domain. Our empirical results on four benchmark datasets suggest that our approach significantly enhances the performance of pretrained QA models on the target domain, and even outperforms models that have access to the source data during adaptation.

cs.CL↗

arXiv2019

An experimental proof that resistance-switching memories are not memristors

It has been suggested that all resistive-switching memory cells are memristors. The latter are hypothetical, ideal devices whose resistance, as originally formulated, depends only on the net charge that traverses them. Recently, an unambiguous test has been proposed [J. Phys. D: Appl. Phys. {\bf 52}, 01LT01 (2019)] to determine whether a given physical system is indeed a memristor or not. Here, we experimentally apply such a test to both in-house fabricated Cu-SiO2 and commercially available electrochemical metallization cells. Our results unambiguously show that electrochemical metallization memory cells are not memristors. Since the particular resistance-switching memories employed in our study share similar features with many other memory cells, our findings refute the claim that all resistance-switching memories are memristors. They also cast doubts on the existence of ideal memristors as actual physical devices that can be fabricated experimentally. Our results then lead us to formulate two memristor impossibility conjectures regarding the impossibility of building a model of physical resistance-switching memories based on the memristor model.

cond-mat.mes-hall↗

arXiv2010

Extraction of crystal-field parameters for lanthanide ions from quantum-chemical calculations

A simple method for constructing effective Hamiltonians for the 4fN and 4fN-15d energy levels of lanthanide ions in crystals from quantum-chemical calculations is presented. The method is demonstrated by deriving crystal-field and spin-orbit parameters for Ce3+ ions doped in LiYF4, Cs2NaYCl6, CaF2, KY3F10 and YAG host crystals from quantum chemical calculations based on the DV-Xα method. Good agreement between calculated and fitted values of the crystal-field parameters is obtained. The method can be used to calculate parameters even for low-symmetry sites where there are more parameters than energy levels.

cond-mat.mtrl-sci↗