Designing and displaying haptic signals with sensory and emotional attributes can improve the user experience in various applications. Free-form user language provides rich sensory and emotional information for haptic design (e.g., ``This signal feels smooth and exciting''), but little work exists on linking user descriptions to haptic signals (i.e., language grounding). To address this gap, we conducted a study where 12 users described the feel of 32 signals perceived on a surface haptics (i.e., electrovibration) display. We developed a computational pipeline using natural language processing (NLP) techniques, such as GPT-3.5 Turbo and word embedding methods, to extract sensory and emotional keywords and group them into semantic clusters (i.e., concepts). We linked the keyword clusters to haptic signal features (e.g., pulse count) using correlation analysis. The proposed pipeline demonstrates the viability of a computational approach to analyzing haptic experiences. We discuss our future plans for creating a predictive model of haptic experience.

Therefore, motivated by these experimental observations, the goal of this work is to demonstrate how different practically feasible As quantum computing architecture matures, it is important to and simple arrangements of neutral atoms can be leveraged to investigate new technologies that lend unique advantages. In this improve the overall execution of quantum circuits in an algorithmspecific work, we propose, Qompose, a neutral atom quantum computing way. However, we show, that this problem poses non-trivial framework for efficiently composing quantum circuits on 2-D challenges due to the inherent complexities of the neutral atombased topologies of neutral atoms. Qompose selects an efficient topology quantum computing architecture and execution of quantum for any given circuit in order to optimize for length of execution circuits. One challenge is selecting a topology from the infinite through efficient parallelism and for overall fidelity.