Xizi Gong | Searcharxiv

arXiv2026

Magical Touch: Transforming Raw Capacitive Streams into Expressive Hand-Touchscreen Interaction

Modern touchscreens utilize capacitive sensing technology to enable precise and robust multi-touch interaction. However, the broader expressive potential of the human hand remains underutilized, since most existing methods directly filter out larger-area hand-screen contact. This paper introduces Magical Touch, an interaction method based on raw capacitive sensing data. By directly integrating raw touchscreen sensor data into the interaction loop, our method allows users to interact with the screen naturally and efficiently using arbitrary hand gestures on existing touchscreen devices. To demonstrate the feasibility and expressive capacity of this approach, we implement a physics-based interactive game featuring single-player, multiplayer collaborative, and pressure-sensitive modes. These scenarios showcase how digital objects can respond in real-time to both the geometry and contact intensity of the user's hand. Our results indicate that leveraging raw capacitive data can expand the design space of touchscreen interaction, offering an embodied and continuous interaction paradigm beyond existing fingertip-based approaches.

cs.HC↗

arXiv2023

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.

q-bio.NC↗