arXiv subjects

Xinyi Cai

Publications and source records attributed to Xinyi Cai.

8 recordsLinked to original sources

arXiv2025

Coupled Modeling of External Pressure and Capillary Blood Flow: Nonlinear Dynamics of Vascular Elasticity and Collapse Effects

External pressure significantly influences microcirculatory capillary blood flow, yet current studies lack quantitative modeling. This work proposes a nonlinear segmented coupling model between external pressure and capillary flow, incorporating vascular elasticity and collapse effects. The pressure-flow response is divided into three phases: elastic compression under low pressure (less than 30 mmHg), elliptical collapse in the transition zone (30 to 40 mmHg), and closure-induced attenuation under high pressure (above 40 mmHg), with explicit expressions derived for each. Parameter sensitivity analysis and comparison with literature demonstrate the model's capability in capturing key determinants. The proposed framework supports dose-response assessment and individualized parameter tuning in pressure-based therapies such as tourniquets and compression garments.

physics.bio-ph↗

arXiv2025

Dimensionality-dependent electronic and vibrational dynamics in low-dimensional organic-inorganic tin halides

Photo-induced dynamics of electronic processes in materials are driven by the coupling between electronic and nuclear degrees of freedom. Here we construct 1D and 2D organic-inorganic tin halides to investigate the functional role of dimensionality to exciton-phonon coupling (EPC) and exciton self-trapping. The results show that the 1D system has strong EPC leading to excitation-independent self-trapped exciton (STE) emission, while the 2D system exhibits over ten times weaker EPC resulting in free exciton emission. By performing femtosecond transient absorption experiments, we directly resolve the room-temperature vibrational wavepackets in the 1D system, some of which propagate along the STE potential energy surface. A combination of wagging and asymmetric stretching motions (~106 cm-1) in tin iodide is identified as such a mode inducing exciton self-trapping. While no room-temperature wavepackets are observed in the 2D system. These findings uncover the interplay between the dimensionality-dependent EPC and electronic/nuclear dynamics, offering constructive guidance to develop multifunctional organic-inorganic metal halides.

physics.chem-ph↗

arXiv2025

Fine-Grained Evaluation for Implicit Discourse Relation Recognition

Implicit discourse relation recognition is a challenging task in discourse analysis due to the absence of explicit discourse connectives between spans of text. Recent pre-trained language models have achieved great success on this task. However, there is no fine-grained analysis of the performance of these pre-trained language models for this task. Therefore, the difficulty and possible directions of this task is unclear. In this paper, we deeply analyze the model prediction, attempting to find out the difficulty for the pre-trained language models and the possible directions of this task. In addition to having an in-depth analysis for this task by using pre-trained language models, we semi-manually annotate data to add relatively high-quality data for the relations with few annotated examples in PDTB 3.0. The annotated data significantly help improve implicit discourse relation recognition for level-2 senses.

cs.CL↗

arXiv2024

Sentiment Analysis of Spanish Political Party Tweets Using Pre-trained Language Models

Title: Sentiment Analysis of Spanish Political Party Communications on Twitter Using Pre-trained Language Models Authors: Chuqiao Song, Shunzhang Chen, Xinyi Cai, Hao Chen Comments: 21 pages, 6 figures Abstract: This study investigates sentiment patterns within Spanish political party communications on Twitter by leveraging BETO and RoBERTuito, two pre-trained language models optimized for Spanish text. Using a dataset of tweets from major Spanish political parties: PSOE, PP, Vox, Podemos, and Ciudadanos, spanning 2019 to 2024, this research analyzes sentiment distributions and explores the relationship between sentiment expression and party ideology. The findings indicate that both models consistently identify a predominant Neutral sentiment across all parties, with significant variations in Negative and Positive sentiments that align with ideological distinctions. Specifically, Vox exhibits higher levels of Negative sentiment, while PSOE demonstrates relatively high Positive sentiment, supporting the hypothesis that emotional appeals in political messaging reflect ideological stances. This study underscores the potential of pre-trained language models for non-English sentiment analysis on social media, providing insights into sentiment dynamics that shape public discourse within Spain's multi-party political system. Keywords: Spanish politics, sentiment analysis, pre-trained language models, Twitter, BETO, RoBERTuito, political ideology, multi-party system

cs.CL↗

arXiv2024

LoCI-DiffCom: Longitudinal Consistency-Informed Diffusion Model for 3D Infant Brain Image Completion

The infant brain undergoes rapid development in the first few years after birth.Compared to cross-sectional studies, longitudinal studies can depict the trajectories of infants brain development with higher accuracy, statistical power and flexibility.However, the collection of infant longitudinal magnetic resonance (MR) data suffers a notorious dropout problem, resulting in incomplete datasets with missing time points. This limitation significantly impedes subsequent neuroscience and clinical modeling. Yet, existing deep generative models are facing difficulties in missing brain image completion, due to sparse data and the nonlinear, dramatic contrast/geometric variations in the developing brain. We propose LoCI-DiffCom, a novel Longitudinal Consistency-Informed Diffusion model for infant brain image Completion,which integrates the images from preceding and subsequent time points to guide a diffusion model for generating high-fidelity missing data. Our designed LoCI module can work on highly sparse sequences, relying solely on data from two temporal points. Despite wide separation and diversity between age time points, our approach can extract individualized developmental features while ensuring context-aware consistency. Our experiments on a large infant brain MR dataset demonstrate its effectiveness with consistent performance on missing infant brain MR completion even in big gap scenarios, aiding in better delineation of early developmental trajectories.

eess.IV↗

arXiv2024

Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion

Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition. Longitudinal magnetic resonance imaging (MRI) is an effective tool to investigate such a crucial stage by capturing the developmental trajectories of the brain structures. However, longitudinal MRI acquisition always meets a serious data-missing problem due to participant dropout and failed scans, making longitudinal infant brain atlas construction and developmental trajectory delineation quite challenging. Thanks to the development of an AI-based generative model, neuroimage completion has become a powerful technique to retain as much available data as possible. However, current image completion methods usually suffer from inconsistency within each individual subject in the time dimension, compromising the overall quality. To solve this problem, our paper proposed a two-stage cascaded diffusion model, Cas-DiffCom, for dense and longitudinal 3D infant brain MRI completion and super-resolution. We applied our proposed method to the Baby Connectome Project (BCP) dataset. The experiment results validate that Cas-DiffCom achieves both individual consistency and high fidelity in longitudinal infant brain image completion. We further applied the generated infant brain images to two downstream tasks, brain tissue segmentation and developmental trajectory delineation, to declare its task-oriented potential in the neuroscience field.

eess.IV↗

arXiv2023

Solvable non-Hermitian skin effects and real-space exceptional points: Non-Hermitian generalized Bloch theorem

Non-Hermitian systems can exhibit extraordinary boundary behaviors, known as the non-Hermitian skin effects, where all the eigenstates are localized exponentially at one side of lattice model. To give a full understanding and control of non-Hermitian skin effects, we have developed the non-Hermitian generalized Bloch theorem to provide the analytical expression for all solvable eigenvalues and eigenstates, in which translation symmetry is broken due to the open boundary condition. By introducing the Vieta's theorem for any polynomial equation with arbitrary degree, our approach is widely applicable for one-dimensional non-Hermitian tight-binding models. With the non-Hermitian generalized Bloch theorem, we can analyze the condition of existence or non-existence of the non-Hermitian skin effects at a mathematically rigorous level. Additionally, the non-Hermitian generalized Bloch theorem allows us to explore the real-space exceptional points. We also establish the connection between our approach and the generalized Brillouin zone method. To illustrate our main results, we examine two concrete examples including the Su-Schrieffer-Heeger chain model with long-range couplings, and the ladder model with non-reciprocal interaction. Our non-Hermitian generalized Bloch theorem provides an efficient way to analytically study various non-Hermitian phenomena in more general cases.

quant-ph↗

arXiv2020

Shallow Discourse Annotation for Chinese TED Talks

Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chinese) that have been annotated with discourse relations in the style of the Penn Discourse TreeBank, adapted to properties of Chinese text that are not present in English. The resource is currently unique in annotating discourse-level properties of planned spoken monologues rather than of written text. An inter-annotator agreement study demonstrates that the annotation scheme is able to achieve highly reliable results.

cs.CL↗