arXiv subjects

Nuoxian Huang

Publications and source records attributed to Nuoxian Huang.

6 recordsLinked to original sources

arXiv2026

nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding

Rotary Position Embedding (RoPE) is widely adopted in Transformer models, yet its extension to high-dimensional domains lacks a unified theoretical formulation. Most existing approaches either apply rotations independently along each axis or empirically mix frequencies, which limits cross-dimensional interactions and yields direction-dependent representations. To address these limitations, we propose nD-RoPE, a decomposition-free generalization of RoPE to arbitrary dimensions. From a translation-invariant formulation in continuous Hilbert space, we derive a spectral condition for isotropy that requires treating positions and frequencies as coupled \(n\)-dimensional vectors. We instantiate this formulation with a multi-scale regular-simplex wave-vector design, which provides non-degenerate spatial coverage and a symmetric, directionally balanced second-order response. Experiments across images, videos, and point clouds demonstrate consistent performance gains and improved generalization in high-dimensional settings.

cs.LG↗

arXiv2026

GridPE: A Grid Cell-Inspired Unified Position Embedding for Arbitrary-Dimensional Spaces

Understanding spatial relationships across all dimensions is fundamental for intelligent systems. However, existing positional embeddings, such as Rotary Positional Embedding (RoPE), lack theoretical guarantees for high-dimensional spatiotemporal tasks like video understanding and robotic navigation. Inspired by the hexagonal periodic coding of grid cells in mammalian spatial cognition, we propose GridPE -- a novel positional embedding framework that integrates computational neuroscience principles with harmonic analysis. Our approach builds upon Random Fourier Features and leverages principles from neuroscience to construct efficient embeddings. Theoretically, we prove that any translation-invariant spatial function can be approximated by a finite sum of Fourier bases, which naturally reduces to RoPE in the one-dimensional case. We then derive the directions and quantities of frequency vectors at each scale in any Euclidean dimension, along with the optimal ratio between different scales, from a bioavailability perspective. These derivations are equivalent to the relationship between the centroid and the vertices of a regular simplex in that dimension. We validate GridPE across a range of spatial modeling tasks, including 2D image classification (ImageNet100) and 3D point cloud recognition (ModelNet40). Our theoretical analysis establishes GridPE as a unified framework for positional embedding in arbitrary-dimensional spaces, while empirical results demonstrate its superiority over existing methods.

cs.NE↗

arXiv2025

AskNearby: An LLM-Based Application for Neighborhood Information Retrieval and Personalized Cognitive-Map Recommendations

The "15-minute city" envisions neighborhoods where residents can meet daily needs via a short walk or bike ride. Realizing this vision requires not only physical proximity but also efficient and reliable access to information about nearby places, services, and events. Existing location-based systems, however, focus mainly on city-level tasks and neglect the spatial, temporal, and cognitive factors that shape localized decision-making. We conceptualize this gap as the Local Life Information Accessibility (LLIA) problem and introduce AskNearby, an AI-driven community application that unifies retrieval and recommendation within the 15-minute life circle. AskNearby integrates (i) a three-layer Retrieval-Augmented Generation (RAG) pipeline that synergizes graph-based, semantic-vector, and geographic retrieval with (ii) a cognitive-map model that encodes each user's neighborhood familiarity and preferences. Experiments on real-world community datasets demonstrate that AskNearby significantly outperforms LLM-based and map-based baselines in retrieval accuracy and recommendation quality, achieving robust performance in spatiotemporal grounding and cognitive-aware ranking. Real-world deployments further validate its effectiveness. By addressing the LLIA challenge, AskNearby empowers residents to more effectively discover local resources, plan daily activities, and engage in community life.

cs.IR↗

arXiv2025

ST-ProC: A Graph-Prototypical Framework for Robust Semi-Supervised Travel Mode Identification

Travel mode identification (TMI) from GPS trajectories is critical for urban intelligence, but is hampered by the high cost of annotation, leading to severe label scarcity. Prevailing semi-supervised learning (SSL) methods are ill-suited for this task, as they suffer from catastrophic confirmation bias and ignore the intrinsic data manifold. We propose ST-ProC, a novel graph-prototypical multi-objective SSL framework to address these limitations. Our framework synergizes a graph-prototypical core with foundational SSL Support. The core exploits the data manifold via graph regularization, prototypical anchoring, and a novel, margin-aware pseudo-labeling strategy to actively reject noise. This core is supported and stabilized by foundational contrastive and teacher-student consistency losses, ensuring high-quality representations and robust optimization. ST-ProC outperforms all baselines by a significant margin, demonstrating its efficacy in real-world sparse-label settings, with a performance boost of 21.5% over state-of-the-art methods like FixMatch.

cs.LG↗

arXiv2024

Unveiling Activity-Travel Patterns through Topological Data Analysis

In the context of rapid urbanization, understanding the patterns of urban residents' activities and mobility is crucial for optimizing transportation systems and enhancing urban management efficiency. This study addresses the limitations of traditional travel analysis methods in handling high-dimensional and large-scale spatiotemporal data by incorporating Topological Data Analysis (TDA) techniques, specifically using persistent homology. This method allows for the extraction of information from the topological structure of data, enabling the effective identification and analysis of complex spatiotemporal behavior patterns without reducing the data's dimensionality. We utilized mobile signaling data from a community in Shenzhen, which includes detailed geographic and temporal information, providing an ideal sample for analyzing urban residents' behavior patterns. Using our pattern mining framework, we successfully identified five main patterns of residents' activities and travel, revealing daily behavioral habits and reflecting the activity heterogeneity among residents with different socio-economic attributes. These findings not only assist urban planners in better session design but also provide new characteristics for predictive mobility models.

stat.AP↗

arXiv2024

PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval

In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT, the first specialized Large Language Model tailored for urban and spatial planning. Developed through collaborative efforts with institutions like the Chinese Academy of Urban Planning, PlanGPT leverages a customized local database retrieval framework, domain-specific fine-tuning of base models, and advanced tooling capabilities. Empirical tests demonstrate that PlanGPT has achieved advanced performance, delivering responses of superior quality precisely tailored to the intricacies of urban planning.

cs.CL↗