This paper presents SurfRadar, a fully automatic mmWave system for in-the-wild surface characterization. SurfRadar operates on a mobile robot and estimates intrinsic surface properties including dielectric constant and roughness. Central to our approach is leveraging high-resolution imaging and analyzing coherent surface reflection images rather than raw radar signals. We develop a physics-based model that connects material parameters to these images. Through forward synthesis and backward optimization, SurfRadar recovers the intrinsic parameters that best explain the observed reflection profile. Our results demonstrate accurate characterization across 11 surface types, the ability to produce scene-level semantic maps, and applicability to other datasets.

Student simulation supports educators to improve teaching by interacting with virtual students.However, most existing approaches ignore the modulation effects of course materials because of two challenges: the lack of datasets with granularly annotated course materials, and the limitation of existing simulation models in processing extremely long textual data.To solve the challenges, we first run a 6-week education workshop from N = 60 students to collect fine-grained data using a custom built online education system, which logs students' learning behaviors as they interact with lecture materials over time.Second, we propose a transferable iterative reflection (TIR) module that augments both prompting-based and finetuning-based large language models (LLMs) for simulating learning behaviors.Our comprehensive experiments show that TIR enables the LLMs to perform more accurate student simulation than classical deep learning models, even with limited demonstration data.Our TIR approach better captures the granular dynamism of learning performance and inter-student correlations in classrooms, paving the way towards a "digital twin" for online education.

Eye blink dynamics offer crucial insights into physiological and cognitive states. Existing solutions either require specialized equipment for detailed measurements or sacrifice temporal resolution for accessibility. We present BlinkWise, the first system that transforms everyday eyewear into a wise device for detailed blink dynamics tracking as a tiny add-on. BlinkWise measures eye openness in real-time on resource-constrained edge devices by leveraging both RF modality's inherent efficiency and novel computational optimizations—including recurrentization of convolutional network operations, quantization-aware normalization, and a lightweight proposal algorithm. Evaluation with 20 subjects and more than 18,000 blink measurements demonstrated BlinkWise's high accuracy in capturing subtle blink dynamics at millisecond resolution, achieving a Pearson correlation of 0.981 with ground truth. Through three real-world application studies, we demonstrate BlinkWise's potential for monitoring cognitive states and ocular health. Code, datasets, and demo videos are available on our website.

Molecular communication has recently gained a lot of interest due to its potential to enable micro-implants to communicate by releasing molecules into the bloodstream. In this paper, we aim to explore the molecular communication channel through theoretical and empirical modeling in order to achieve a better understanding of its characteristics, which tend to be more complex in practice than traditional wireless and wired channels. Our study reveals two key new characteristics that have been overlooked by past work. Specifically, the molecular communication channel exhibits non-causal inter-symbol-interference and a long delay spread, that extends beyond the channel coherence time, which limit decoding performance. To address this, we design, μ-Link a molecular communication protocol and decoder that accounts for these new insights. We build a testbed to experimentally validate our findings and show that μ-Link can improve the achievable data rates with significantly lower bit error rates.