arXiv subjects

Jih-Kang Hsieh

Publications and source records attributed to Jih-Kang Hsieh.

2 recordsLinked to original sources

arXiv2025

Liquid Droplet as Adaptive Material while Levitating via Coupling between Plasma and Kelvin Force

Fascinating in art and science, the ability to float is also captivating and relevant in practical applications, such as Penning and ion traps that are fundamental to quantum computing. In this work, we first reproduce the classic water bridge by glycerol and, as it breaks down due to thermal agitation, observe that a lump of glycerol with mass~2.5 g can float and exhibit near-periodic oscillations. Through experiments, finite element analysis, and simulations, we discover that the stability of the floating droplet is made possible by the interaction between three mechanisms: Deformation, Plasma, and Kelvin force. Note that glycerol cluster (GC) falls in the class of adaptive materials that can change their properties or behavior in response to varying environmental conditions, i.e., stimuli-responsive. Furthermore, the stimuli, modified by the deformation of GC, collaborate with it to create this unique simple, yet stable, floating system. Backed up by simulations, this process, operated by only a single pair of electrodes, holds the potential to develop a simple yet powerful railgun.

cond-mat.soft↗

arXiv2025

Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval

Text-to-image retrieval (TIR) aims to find relevant images based on a textual query, but existing approaches are primarily based on whole-image captions and lack interpretability. Meanwhile, referring expression segmentation (RES) enables precise object localization based on natural language descriptions but is computationally expensive when applied across large image collections. To bridge this gap, we introduce Mask-aware TIR (MaTIR), a new task that unifies TIR and RES, requiring both efficient image search and accurate object segmentation. To address this task, we propose a two-stage framework, comprising a first stage for segmentation-aware image retrieval and a second stage for reranking and object grounding with a multimodal large language model (MLLM). We leverage SAM 2 to generate object masks and Alpha-CLIP to extract region-level embeddings offline at first, enabling effective and scalable online retrieval. Secondly, MLLM is used to refine retrieval rankings and generate bounding boxes, which are matched to segmentation masks. We evaluate our approach on COCO and D$^3$ datasets, demonstrating significant improvements in both retrieval accuracy and segmentation quality over previous methods.

cs.CV↗