arXiv subjects

Sihang Wang

Publications and source records attributed to Sihang Wang.

3 recordsLinked to original sources

arXiv2026

First-principles study of photovoltaic and thermoelectric properties of AgBiSCl2

This work systematically investigates the potential of the hybrid anion semiconductor AgBiSCl2 for photovoltaic and thermoelectric applications, aiming to provide theoretical guidance for high-performance energy conversion devices. Structural analysis reveals favorable ductility and a relatively low Debye temperature. Analysis of interatomic interactions indicates that Ag-S and Ag-Cl bonds are relatively weak, resulting in local structural softness and enhanced lattice anharmonicity. These weak bonds facilitate phonon scattering and give rise to low-frequency localized rattling vibrations primarily associated with Ag atoms, contributing to reduced lattice thermal conductivity. In contrast, Bi-S bonds exhibit stronger, directional interactions, which help stabilize the overall structure. The coexistence of weak bonding and strong lattice coupling enables favorable modulation of thermal transport properties.Optically, AgBiSCl2 possesses a high static dielectric constant and exhibits strong absorption in the ultraviolet region. In terms of thermal transport, phonon spectrum exhibit mode hardening with temperature increasing. The localized Ag vibrations intensify the anharmonicity, reducing phonon lifetimes and group velocities.For electronic transport, the p-type material maintains a higher Seebeck coefficient than the n-type, while the latter shows greater electrical conductivity. At 700 K, the figure of merit reaches 0.77 for p-type and 0.69 for n-type AgBiSCl2, indicating promising high-temperature thermoelectric performance.In summary, AgBiSCl2 exhibits excellent potential for dual photovoltaic and thermoelectric applications. Its unique bonding features and lattice response mechanisms offer valuable insights into designing multifunctional energy conversion materials.

cond-mat.mtrl-sci↗

arXiv2025

Enhancing Hyperbole and Metaphor Detection with Their Bidirectional Dynamic Interaction and Emotion Knowledge

Text-based hyperbole and metaphor detection are of great significance for natural language processing (NLP) tasks. However, due to their semantic obscurity and expressive diversity, it is rather challenging to identify them. Existing methods mostly focus on superficial text features, ignoring the associations of hyperbole and metaphor as well as the effect of implicit emotion on perceiving these rhetorical devices. To implement these hypotheses, we propose an emotion-guided hyperbole and metaphor detection framework based on bidirectional dynamic interaction (EmoBi). Firstly, the emotion analysis module deeply mines the emotion connotations behind hyperbole and metaphor. Next, the emotion-based domain mapping module identifies the target and source domains to gain a deeper understanding of the implicit meanings of hyperbole and metaphor. Finally, the bidirectional dynamic interaction module enables the mutual promotion between hyperbole and metaphor. Meanwhile, a verification mechanism is designed to ensure detection accuracy and reliability. Experiments show that EmoBi outperforms all baseline methods on four datasets. Specifically, compared to the current SoTA, the F1 score increased by 28.1% for hyperbole detection on the TroFi dataset and 23.1% for metaphor detection on the HYPO-L dataset. These results, underpinned by in-depth analyses, underscore the effectiveness and potential of our approach for advancing hyperbole and metaphor detection.

cs.CL↗

arXiv2021

Multi-Modality Cardiac Image Analysis with Deep Learning

Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is a promising technique to visualize and quantify myocardial infarction (MI) and atrial scars. Automating quantification of MI and atrial scars can be challenging due to the low image quality and complex enhancement patterns of LGE MRI. Moreover, compared with the other sequences LGE MRIs with gold standard labels are particularly limited, which represents another obstacle for developing novel algorithms for automatic segmentation and quantification of LGE MRIs. This chapter aims to summarize the state-of-the-art and our recent advanced contributions on deep learning based multi-modality cardiac image analysis. Firstly, we introduce two benchmark works for multi-sequence cardiac MRI based myocardial and pathology segmentation. Secondly, two novel frameworks for left atrial scar segmentation and quantification from LGE MRI were presented. Thirdly, we present three unsupervised domain adaptation techniques for cross-modality cardiac image segmentation.

eess.IV↗