arXiv subjects

M. Cynthia Hipwell

Publications and source records attributed to M. Cynthia Hipwell.

4 recordsLinked to original sources

arXiv2026

A Hybrid Soft Haptic Display for Rendering Lump Stiffness in Remote Palpation

Remote palpation enables noninvasive tissue examination in telemedicine, yet current tactile displays often lack the fidelity to convey both large-scale forces and fine spatial details. This study introduces a hybrid fingertip display comprising a rigid platform and a $4\times4$ soft pneumatic tactile display (4.93 mm displacement and 1.175 N per single pneumatic chamber) to render a hard lump beneath soft tissue. This study compares three rendering strategies: a Platform-Only baseline that renders the total interaction force; a Hybrid A (Position + Force Feedback) strategy that adds a dynamic, real-time soft spatial cue; and a Hybrid B (Position + Preloaded Stiffness Feedback) strategy that provides a constant, pre-calculated soft spatial cue. In a 12-participant lump detection study, both hybrid methods dramatically improved accuracy over the Platform-Only baseline (from 50\% to over 95\%). While the Hybrid B was highlighted qualitatively for realism, its event-based averaging is expected to increase interaction latency in real-time operation. This suggests a trade-off between perceived lump realism and real-time responsiveness, such that rendering choices that enhance realism may conflict with those that minimize latency.

cs.HC↗

arXiv2024

Automatic assessment of text-based responses in post-secondary education: A systematic review

Text-based open-ended questions in academic formative and summative assessments help students become deep learners and prepare them to understand concepts for a subsequent conceptual assessment. However, grading text-based questions, especially in large courses, is tedious and time-consuming for instructors. Text processing models continue progressing with the rapid development of Artificial Intelligence (AI) tools and Natural Language Processing (NLP) algorithms. Especially after breakthroughs in Large Language Models (LLM), there is immense potential to automate rapid assessment and feedback of text-based responses in education. This systematic review adopts a scientific and reproducible literature search strategy based on the PRISMA process using explicit inclusion and exclusion criteria to study text-based automatic assessment systems in post-secondary education, screening 838 papers and synthesizing 93 studies. To understand how text-based automatic assessment systems have been developed and applied in education in recent years, three research questions are considered. All included studies are summarized and categorized according to a proposed comprehensive framework, including the input and output of the system, research motivation, and research outcomes, aiming to answer the research questions accordingly. Additionally, the typical studies of automated assessment systems, research methods, and application domains in these studies are investigated and summarized. This systematic review provides an overview of recent educational applications of text-based assessment systems for understanding the latest AI/NLP developments assisting in text-based assessments in higher education. Findings will particularly benefit researchers and educators incorporating LLMs such as ChatGPT into their educational activities.

cs.CY↗

arXiv2021

Highly directional and coherent emission from dark excitons enabled by bound states in the continuum

A double-edged sword in two-dimensional material science and technology is an optically forbidden dark exciton. On the one hand, it is fascinating for condensed matter physics, quantum information processing, and optoelectronics due to its long lifetime. On the other hand, it is notorious for being optically inaccessible from both excitation and detection standpoints. Here, we provide an efficient and low-loss solution to the dilemma by reintroducing photonics bound states in the continuum (BICs) to manipulate dark excitons in the momentum space. In a monolayer tungsten diselenide under normal incidence, we observed a giant enhancement with an enhancement factor of ~3,100 for dark excitons enabled by transverse magnetic BICs with intrinsic out-of-plane electric fields. By further employing widely tunable Friedrich-Wintgen BICs, we demonstrated highly directional emission from the dark excitons with a divergence angle of merely 7 degrees. We found that the directional emission is coherent at room temperature, unambiguously shown in polarization analyses and interference measurements. Therefore, the BICs reintroduced as a momentum-space photonic environment could be an intriguing platform to reshape and redefine light-matter interactions in nearby quantum materials, such as low-dimensional materials, otherwise challenging or even impossible to achieve.

physics.optics↗

arXiv2021

A universal deep learning strategy for designing high-quality-factor photonic resonances

Resonance is instrumental in modern optics and photonics for novel phenomena such as cavity quantum electrodynamics and electric-field-induced transparency. While one can use numerical simulations to sweep geometric and material parameters of optical structures, these simulations usually require considerably long calculation time (spanning from several hours to several weeks) and substantial computational resources. Such requirements significantly limit their applicability in understanding and inverse designing structures with desired resonance performances. Recently, the introduction of artificial intelligence allows for faster predictions of resonance with less demanding computational requirements. However, current end-to-end deep learning approaches generally fail to predict resonances with high quality-factors (Q-factor). Here, we introduce a universal deep learning strategy that can predict ultra-high Q-factor resonances by decomposing spectra with an adaptive data acquisition (ADA) method while incorporating resonance information. We exploit bound states in the continuum (BICs) with an infinite Q-factor to testify this resonance-informed deep learning (RIDL) strategy. The trained RIDL strategy achieves high-accuracy prediction of reflection spectra and photonic band structures while using a considerably small training dataset. We further develop an inverse design algorithm based on the RIDL strategy for a symmetry-protected BIC on a suspended silicon nitride photonic crystal (PhC) slab. The predicted and measured angle-resolved band structures show minimum differences. We expect the RIDL strategy to apply to many other physical phenomena which exhibit Gaussian, Lorentzian, and Fano resonances.

physics.optics↗