arXiv subjects

Qiong Ye

Publications and source records attributed to Qiong Ye.

5 recordsLinked to original sources

arXiv2025

Synthetic Fluency and Epistemic Offloading in Undergraduate Mathematics in the Age of AI

The rapid adoption of generative artificial intelligence (AI) tools in higher education is transforming how students engage with undergraduate mathematics, raising concerns about learning and assessment validity. This study examines the impact of AI accessibility across a two-semester, multi-course dataset including Business Calculus, Linear Algebra, and Calculus III. By comparing unproctored homework and proctored exam performance, we analyze how student learning behaviors shift in AI-accessible environments, particularly through epistemic off-loading of mathematical work. Guided by a sociocognitive framework, we employ complementary measures -- performance gaps, homework-exam correlations, and Wasserstein distance -- to characterize divergence between practice and mastery. Results reveal a growing integrity gap as course content shifts from procedural to conceptual and spatially intensive mathematics. In both Business Calculus and Linear Algebra, differences in homework format (online versus hand-written, TA-graded) do not yield substantively different performance patterns, indicating that paper-based homework is not inherently more resistant to AI-mediated offloading. While homework retains partial predictive validity in procedural courses, upper-division courses exhibit a collapse in alignment between homework and exams, indicating that unproctored assessments increasingly reflect synthetic fluency rather than internalized understanding. These findings highlight the need to rethink assessment practices in the AI era.

math.HO↗

arXiv2025

Experimental and numerical simulation study on the thermal performance of building envelope structures incorporating the solid-solid phase change material

This work is an experimental and numerical study of the thermal performance of building envelope structures incorporating a solid-solid phase change material (S-S PCM), consisting in a cross-linked polyurethane designated as PUX-1500-20. This S-S PCM is capable of storing and releasing thermal energy via phase transitions within the human comfort temperature range, facilitating the temporal and spatial transfer of solar energy for optimizing energy efficiency. The primary aim of this work is to integrate the S-S PCM into hollow bricks used in building envelopes and to evaluate their thermal inertia through both experimental testing and numerical simulation. The experimental results demonstrate that the integration of the PCM effectively delays and decreases the indoor temperature peak. The simulation results also show that the incorporation of the S-S PCM into hollow bricks gives rise to a phase shift of 7 hours and a decrement factor of 0.38. In comparison with the thermal behavior of the building envelopes (hollow brick) without PCMs, our results provide convincing evidence of the important thermal inertia of these structures incorporating the PCMs, revealing their significant potential in reducing energy consumption of building.

cs.CE↗

arXiv2023

Characterize the non-Gaussian diffusion property of cerebrospinal fluid using Diffusion Kurtosis Imaging and explore its diagnostic efficacy for Alzheimer's disease

Differentiating Alzheimer's disease (AD) patients from healthy controls (HCs) remains a challenge. The changes of protein level in cerebrospinal fluid (CSF) of AD patients have been reported in the literature. Macromolecules will hinder the movement of water in CSF and lead to non-Gaussian diffusion. Diffusion kurtosis imaging (DKI) is a commonly used technique for quantifying non-Gaussian diffusivity. In this study, we used DKI to evaluate the non-Gaussian diffusion of CSF in AD patients and HC. Between-group difference was explored. In addition, we have built a prediction model using cross-validation Support Vector Machines (SVM), and achieved excellent performance. The validated area under the receiver operating characteristic curve(AUC) is in the range of 0.96-1.00, and the correct prediction is in the range of 87.1% - 90.0%.

q-bio.NC↗

arXiv2022

In vivo labeling and quantitative imaging of neurons using MRI

Mammalian brain is a complex organ that contains billions of neurons. These neurons form various neural circuits that control the perception, cognition, emotion and behavior. Developing in vivo neuronal labeling and imaging techniques is crucial for studying the structure and function of neural circuits. In vivo techniques can provide true physiological information that cannot be provided by ex vivo methods. In this study, we describe a new strategy for in vivo neuronal labeling and quantification using MRI. To demonstrate the ability of this new method, we used neurotropic virus to deliver oatp1a1 gene to the target neural circuit. OATP1A1 protein is expressed on the neuronal membrane and can increase the uptake of a specific MRI contrast agent (Gd-EOB-DTPA). By using T1-weighted images for observation, labeled neurons "light up" on MRI. We further use a dynamic-contrast-enhancement based method to obtain measures that provide quantitative information of labeled neurons in vivo.

q-bio.NC↗

arXiv2017

PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI

Semantic segmentation of functional magnetic resonance imaging (fMRI) makes great sense for pathology diagnosis and decision system of medical robots. The multi-channel fMRI provides more information of the pathological features. But the increased amount of data causes complexity in feature detections. This paper proposes a principal component analysis (PCA)-aided fully convolutional network to particularly deal with multi-channel fMRI. We transfer the learned weights of contemporary classification networks to the segmentation task by fine-tuning. The results of the convolutional network are compared with various methods e.g. k-NN. A new labeling strategy is proposed to solve the semantic segmentation problem with unclear boundaries. Even with a small-sized training dataset, the test results demonstrate that our model outperforms other pathological feature detection methods. Besides, its forward inference only takes 90 milliseconds for a single set of fMRI data. To our knowledge, this is the first time to realize pixel-wise labeling of multi-channel magnetic resonance image using FCN.

cs.CV↗