arXiv subjects

Yueting Li

Publications and source records attributed to Yueting Li.

6 recordsLinked to original sources

arXiv2026

Interface-Engineered Giant Multistate Resistance Switching in Altermagnetic CrSb Multiferroic Tunnel Junctions

Altermagnets enable spin-split transport without stray magnetic fields, yet converting their momentum-dependent spin splitting into a strong tunnel-junction response requires interface-selected tunneling channels. Here, using density functional theory combined with nonequilibrium Green's function calculations, we demonstrate giant multistate resistance switching in CrSb/$\alpha$-In$_2$Se$_3$ altermagnetic multiferroic tunnel junctions. The response is governed not by the bulk spin splitting of CrSb alone, but by a symmetry-selected interfacial mechanism in which Cr/Sb terminations and \textit{h}-BN or graphene insertion layers determine spin-channel matching, while ferroelectric polarization reshapes the electrostatic barrier. Symmetric and asymmetric terminations reverse the correspondence between parallel/antiparallel N\'eel-vector configurations and high-/low-resistance states, showing that the actual alignment of interfacial Cr moments selects the dominant tunneling channels. Monolayer-In$_2$Se$_3$ junctions exhibit four nonvolatile resistance states, with tunneling magnetoresistance (TMR) and tunneling electroresistance (TER) reaching 1626\% and 2206\%, respectively, and increasing to 9576\% and 4144\% upon Fermi-level shifting. Finite-bias calculations further reveal robust spin filtering and tunable spin-polarized currents. Extending the barrier to bilayer In$_2$Se$_3$ introduces interlayer polarization coupling, enabling eight resistance states with maximum TMR and TER values of $3.77\times10^{4}\%$ and $4.18\times10^{5}\%$, respectively. These results establish interface symmetry, spin-channel matching, and ferroelectric barrier reconstruction as design principles for stray-field-free multistate spintronic tunnel devices.

cond-mat.mtrl-sci↗

arXiv2026

The Breakthrough of Sleep: A Contactless Approach for Accurate Sleep Stage Detection Using the Sleepal AI Lamp

Sleep staging is essential for the assessment of sleep quality and the diagnosis of sleep-related disorders. Conventional polysomnography (PSG), while considered the gold standard, is intrusive, labor-intensive, and unsuitable for long-term monitoring. This study evaluates the performance of the Sleepal AI Lamp, a contactless, radar-based consumer-grade sleep tracker, in comparison with gold-standard polysomnography (PSG), using a large-scale dataset comprising 1022 overnight recordings. We extract multi-scale respiratory and motion-related features from radar signals to train a frequency-augmented deep learning model. For the binary sleep-wake classification task, experimental results demonstrated that the model achieved an accuracy of 92.8% alongside a macro-averaged F1 score of 0.895. For four-stage classification (wake, light NREM (N1 + N2), deep NREM (N3), REM), the model achieved an accuracy of 78.5% with a Cohen's kappa coefficient of 0.695 in healthy individuals and maintained a stable accuracy of 77.2% with a kappa of 0.677 in a heterogeneous population including patients with varying severities of obstructive sleep apnea (OSA). These experimental results demonstrate that the sleep staging performance of the contactless Sleepal AI Lamp is in high agreement with expert-labeled PSG sleep stages. Our findings suggest that non-contact radar sensing, combined with advanced temporal modeling, can provide reliable sleep staging performance without requiring physical contact or wearable devices. Owing to its unobtrusive nature, ease of deployment, and robustness to long-term use, the contactless Sleepal AI Lamp shows strong potential for clinical screening, home-based sleep assessment, and continuous longitudinal sleep monitoring in real-world medical and healthcare applications.

eess.SP↗

arXiv2025

Rhetorical Text-to-Image Generation via Two-layer Diffusion Policy Optimization

Generating images from rhetorical languages remains a critical challenge for text-to-image models. Even state-of-the-art (SOTA) multimodal large language models (MLLM) fail to generate images based on the hidden meaning inherent in rhetorical language--despite such content being readily mappable to visual representations by humans. A key limitation is that current models emphasize object-level word embedding alignment, causing metaphorical expressions to steer image generation towards their literal visuals and overlook the intended semantic meaning. To address this, we propose Rhet2Pix, a framework that formulates rhetorical text-to-image generation as a multi-step policy optimization problem, incorporating a two-layer MDP diffusion module. In the outer layer, Rhet2Pix converts the input prompt into incrementally elaborated sub-sentences and executes corresponding image-generation actions, constructing semantically richer visuals. In the inner layer, Rhet2Pix mitigates reward sparsity during image generation by discounting the final reward and optimizing every adjacent action pair along the diffusion denoising trajectory. Extensive experiments demonstrate the effectiveness of Rhet2Pix in rhetorical text-to-image generation. Our model outperforms SOTA MLLMs such as GPT-4o, Grok-3 and leading academic baselines across both qualitative and quantitative evaluations. The code and dataset used in this work are publicly available.

cs.CV↗

arXiv2022

Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome

The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate structural and functional networks quantified by diffusion tensor imaging and resting-state functional MRI. As one of the state-of-the-art approaches for network analysis, graph convolutional networks (GCN) have been separately used to analyze functional and structural networks, but have not been applied to explore inter-network relationships. In this work, we propose to couple the two networks of an individual by adding inter-network edges between corresponding brain regions, so that the joint structure-function graph can be directly analyzed by a single GCN. The weights of inter-network edges are learnable, reflecting non-uniform structure-function coupling strength across the brain. We apply our Joint-GCN to predict age and sex of 662 participants from the public dataset of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) based on their functional and micro-structural white-matter networks. Our results support that the proposed Joint-GCN outperforms existing multi-modal graph learning approaches for analyzing structural and functional networks.

q-bio.NC↗

arXiv2021

Deconvolutional Networks on Graph Data

In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct the input graph signal?" We propose Graph Deconvolutional Network (GDN) and motivate the design of GDN via a combination of inverse filters in spectral domain and de-noising layers in wavelet domain, as the inverse operation results in a high frequency amplifier and may amplify the noise. We demonstrate the effectiveness of the proposed method on several tasks including graph feature imputation and graph structure generation.

cs.LG↗

arXiv2019

Triangle decompositions of $λK_v-λK_w-λK_u$

Denote by $λK_v$ the complete graph of order $v$ with multiplicity $λ$. Let $λK_v-λK_w-λK_u$ be the graph obtained from $λK_v$ by the removal of the edges of two vertex disjoint complete multi-subgraphs with multiplicity $ λ$ of orders $ w $ and $ u $, respectively. When $λ$ is odd, it is shown that there exists a triangle decomposition of $λK_v-λK_w-λK_u$ if and only if $v\geq w+u+\max\{u,w\}$, $ λ\left({v\choose 2}-{u\choose 2}-{w\choose 2}\right) \equiv 0 \pmod 3$ and $λ(v-w) \equiv λ(v-u) \equiv λ(v-1) \equiv 0 \pmod 2$. When $λ$ is even, it is shown that for large enough $v$, the elementary necessary conditions for the existence of a triangle decomposition of $λK_v-λK_w-λK_u$ are also sufficient.

math.CO↗