arXiv subjects

Jiacheng Yu

Publications and source records attributed to Jiacheng Yu.

5 recordsLinked to original sources

arXiv2026

Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation

While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acquisition time in PET-MR scanning is a major obstacle in more efficient clinical practice. Deep learning-based MRI translation provides a potential solution to reduce scan duration. However, current models often focus on specific anatomical regions and face challenges for whole-body scans that consists of highly heterogeneous feature distributions mainly due to (1) different anatomical regions across whole-body, and (2) lesions or pathological tissues. This paper tackles the challenges through a novel Heterogeneity-Adaptive Diffusion Schrodinger Bridge (HA-DSB) framework. By explicitly modeling translation as stochastic transport between source and target distributions, HA-DSB incorporates region context embeddings derived from a vision-language model (VLM) to enable region-specific modeling. To enhance fidelity of the pathological tissue, lesion-aware metabolic prior from PET is integrated directly into the bridge dynamics through a dual-stage guidance mechanism. Specifically, a PET-guided noise modulation module adaptively scales spatial diffusion perturbations during the forward process, while PET features are leveraged during the reverse process to selectively amplify lesion-relevant structures via an attention mechanism. Experiments demonstrate the superiority of our method across different body regions in whole-body MRI translation and show improved translation quality in lesion areas under PET guidance. Our code is available at Github.

cs.CV↗

arXiv2024

Datasets of Visualization for Machine Learning

Datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization datasets and provide a comprehensive overview of existing visualization datasets, including their data types, formats, supported tasks, and openness. We propose a what-why-how model for visualization datasets, considering the content of the dataset (what), the supported tasks (why), and the dataset construction process (how). This model provides a clear understanding of the diversity and complexity of visualization datasets. Additionally, we highlight the challenges faced by existing visualization datasets, including the lack of standardization in data types and formats and the limited availability of large-scale datasets. To address these challenges, we suggest future research directions.

cs.HC↗

arXiv2024

Origin of the Apparent Electric-Field Dependence of Electrostrictive Coefficients

Electrostrictive materials exhibit a strain that is proportional to the square of the induced polarization. In linear dielectrics where the permittivity is constant, this electromechanical strain is also proportional to the square of the electric field. However, under increasing amplitudes of the driving field, the electromechanical strain sometimes saturates; the electrostrictive coefficients therefore appear to depend on the amplitude of the electric field used to measure them. Here, we present a methodology showing that this apparent field dependence is a consequence of neglecting higher-order electromechanical phenomena. When these are taken into account, not only do the electrostrictive coefficients remain constant but the signs of the high-order coefficients enable the prediction of the saturation behavior from a single measurement. We illustrate this approach on both classical and non-classical (so-called ``giant'') electrostrictors.

cond-mat.mtrl-sci↗

arXiv2024

Breathing New Life into Existing Visualizations: A Natural Language-Driven Manipulation Framework

We propose an approach to manipulate existing interactive visualizations to answer users' natural language queries. We analyze the natural language tasks and propose a design space of a hierarchical task structure, which allows for a systematic decomposition of complex queries. We introduce a four-level visualization manipulation space to facilitate in-situ manipulations for visualizations, enabling a fine-grained control over the visualization elements. Our methods comprise two essential components: the natural language-to-task translator and the visualization manipulation parser. The natural language-to-task translator employs advanced NLP techniques to extract structured, hierarchical tasks from natural language queries, even those with varying degrees of ambiguity. The visualization manipulation parser leverages the hierarchical task structure to streamline these tasks into a sequence of atomic visualization manipulations. To illustrate the effectiveness of our approach, we provide real-world examples and experimental results. The evaluation highlights the precision of our natural language parsing capabilities and underscores the smooth transformation of visualization manipulations.

cs.HC↗

arXiv2021

Defining "Giant" Electrostriction

The recent discovery of giant electrostrictors has re-ignited the interest in electrostriction, an electromechanical coupling existing in all dielectrics but overshadowed by its linear counterpart: piezoelectricity. In this review, after a reminder of classical electrostriction, we propose a definition of giant electrostriction based on two empirical relations (Newnam relation and one we propose). From this definition, we review previous reports on giant electrostrictors, to assess their nature. Focusing on the ones satisfying our definition, we compare their performances and characteristics. We also identify some of the hurdles to their adoption in the wide range of electromechanical applications, despite their fundamental and applicative interests.

cond-mat.mtrl-sci↗