Tootfinder

A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
Rongqian Chen, Allison Andreyev, Yanming Xiu, Mahdi Imani, Bin Li, Maria Gorlatova, Gang Tan, Tian Lan
https://arxiv.org/abs/2508.09185

A Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
Augmented Reality (AR) enriches perception by overlaying virtual elements on the physical world. Due to its growing popularity, cognitive attacks that alter AR content to manipulate users' semantic perception have received increasing attention. Existing detection methods often focus on visual changes, which are restricted to pixel- or image-level processing and lack semantic reasoning capabilities, or they rely on pre-trained vision-language models (VLMs), which function as black-box approaches…

HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Rajat Bhattacharjya, Woohyeok Park, Arnab Sarkar, Hyunwoo Oh, Mohsen Imani, Nikil Dutt
https://arxiv.org/abs/2510.10718

HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Direction of Arrival (DoA) estimation techniques face a critical trade-off, as classical methods often lack accuracy in challenging, low signal-to-noise ratio (SNR) conditions, while modern deep learning approaches are too energy-intensive and opaque for resource-constrained, safety-critical systems. We introduce HYPERDOA, a novel estimator leveraging Hyperdimensional Computing (HDC). The framework introduces two distinct feature extraction strategies -- Mean Spatial-Lag Autocorrelation and Spa…

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
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- HYPERDOA: Robust and Efficient DoA Estimation using Hyperdimensional Computing
Rajat Bhattacharjya, Woohyeok Park, Arnab Sarkar, Hyunwoo Oh, Mohsen Imani, Nikil Dutt

Extremely Large-Scale Dynamic Metasurface Antennas for 6G Near-Field Networks: Opportunities and Challenges
Haiyang Zhang, Nir Shlezinger, Giulia Torcolacci, Francesco Guidi, Anna Guerra, Qianyu Yang, Mohammadreza F. Imani, Davide Dardari, Yonina C. Eldar
https://arxiv.org/abs/2508.06952

Extremely Large-Scale Dynamic Metasurface Antennas for 6G Near-Field Networks: Opportunities and Challenges
6G networks will need to support higher data rates, high-precision localization, and imaging capabilities. Near-field technologies, enabled by extremely large-scale (XL)-arrays, are expected to be essential physical-layer solutions to meet these ambitious requirements. However, implementing XL-array systems using traditional fully-digital or hybrid analog/digital architectures poses significant challenges due to high power consumption and implementation costs. Emerging XL-dynamic metasurface an…

MissionHD: Data-Driven Refinement of Reasoning Graph Structure through Hyperdimensional Causal Path Encoding and Decoding
Sanggeon Yun, Raheeb Hassan, Ryozo Masukawa, Mohsen Imani
https://arxiv.org/abs/2508.14746

MissionHD: Data-Driven Refinement of Reasoning Graph Structure through Hyperdimensional Causal Path Encoding and Decoding
Reasoning graphs from Large Language Models (LLMs) are often misaligned with downstream visual tasks such as video anomaly detection (VAD). Existing Graph Structure Refinement (GSR) methods are ill-suited for these novel, dataset-less graphs. We introduce Data-driven GSR (D-GSR), a new paradigm that directly optimizes graph structure using downstream task data, and propose MissionHD, a hyperdimensional computing (HDC) framework to operationalize it. MissionHD uses an efficient encode-decode pro…