Searcharxiv⌕ Search

arXiv subjects

Sitong Li

Publications and source records attributed to Sitong Li.

7 recordsLinked to original sources
arXiv2026

FLAP: FOV-Constrained Active Perception Planning for Prior-Map-Free 3D Navigation

Safe and efficient trajectory planning in unknown, cluttered 3D environments constitutes a critical bottleneck for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications. This challenge is further exacerbated by the limited field-of-view (FOV) and sensing range of onboard sensors. Many existing methods either make simplistic assumptions about unexplored space or rely on conservative heuristics such as speed limits or fixed perception patterns, reducing efficiency and generalizing poorly across different sensor types. In this work, we propose a novel planning framework that directly integrates active perception into trajectory optimization, thereby improving safety while preserving efficiency. The perception constraints are derived from the UAV's dynamic model and formulated in the sensor coordinate frame, which enables precise handling of FOV geometry. The velocity-triggered activation mechanism enables the planner to balance perception and motion efficiency. We introduce an active perception sub-trajectory segment with parametric start-time optimization, mitigating collision risks from late obstacle detection. Our formulation enables active perception during arbitrary 3D maneuvers, extending beyond prior methods designed mainly for horizontal motion. All constraints and penalties are incorporated into a differentiable optimization problem, so the planner requires only a simple front-end global path for guidance, rather than a computationally expensive perception-aware path generator. Extensive simulations and real-world experiments demonstrate robust performance across diverse unknown environments with varying sensor configurations.

cs.RO↗
arXiv2025

MegaCacheX: Towards Cost-Effective Hierarchical Collaborative Content Caching in Emerging Mega-Constellations

Significant latency in global content delivery primarily arises from insufficient terrestrial infrastructure. Deploying space-based content delivery networks within emerging mega-constellations provides an effective means to bridge the digital divide. However, space-based caching faces constraints from physical-layer dynamics, including dynamic topologies, time-varying inter-satellite link conditions, and limited onboard energy. In addition, existing mechanisms often lack fine-grained content categorization and global optimization. This paper proposes MegaCacheX, a cost-effective hierarchical framework for collaborative content distribution that achieves "Earth-independence" by providing cloud services directly from space. Specifically, data centers in Sun-synchronous orbit act as primary content sources, while caching nodes in mega-constellations and ground stations collaboratively form a distributed edge layer. MegaCacheX optimizes caching strategies by integrating content popularity, regional user distribution, and satellite trajectory predictions. Multi-tier caching nodes serve as service anchors, enabling seamless content delivery with low latency. A prototype implemented on a microservices-based, containerized testbed demonstrates that MegaCacheX reduces global content access latency by about 36% compared to baseline approaches, while maintaining cost efficiency.

eess.SY↗
arXiv2025

A Review of LLM-Assisted Ideation

We present a comprehensive, in-depth review of ideation assisted by large language models (LLMs), highlighting emerging trends and identifying unaddressed research gaps. In total, we examined 61 studies investigating the application of LLMs in both group and individual ideation processes. From these studies, we derived the Hourglass Ideation Framework for LLM-assisted ideation, comprising three phases and seven key ideation stages, which served as the basis for our systematic survey. Our analysis reveals that LLMs are most frequently used for idea generation and refinement, but their use in scope specification, foundational material structuring and multi-idea evaluation and selection remains limited. We provide our findings in extensive tabular and online formats. These catalogues detail research on LLM-assisted, purely LLM-based, and human-only activities across the seven ideation stages for each of the 61 studies. These also detail creative domains, publication outlets, interaction designs, user study designs, and assessment methods. Our analysis of system interaction design reveals a predominant focus on supporting individual ideation activities and text-based interaction, with a growing trend of incorporating multimedia elements. However, in group ideation, tools and interaction modalities targeting both synchronous and asynchronous collaboration are much scarcer. We synthesize the primary findings of our review and outline promising directions for future research in LLM-assisted ideation. We hope this review will help researchers quickly gain an overview of this rapidly expanding area, efficiently locate relevant work, and identify underexplored areas for further investigation. In addition, we believe the framework we present here will form the basis for the development of future problem and solution space taxonomies, and methodologies for LLM-assisted ideation development and use.

cs.HC↗
arXiv2024

Enhancing Out-of-Distribution Detection with Multitesting-based Layer-wise Feature Fusion

Deploying machine learning in open environments presents the challenge of encountering diverse test inputs that differ significantly from the training data. These out-of-distribution samples may exhibit shifts in local or global features compared to the training distribution. The machine learning (ML) community has responded with a number of methods aimed at distinguishing anomalous inputs from original training data. However, the majority of previous studies have primarily focused on the output layer or penultimate layer of pre-trained deep neural networks. In this paper, we propose a novel framework, Multitesting-based Layer-wise Out-of-Distribution (OOD) Detection (MLOD), to identify distributional shifts in test samples at different levels of features through rigorous multiple testing procedure. Our approach distinguishes itself from existing methods as it does not require modifying the structure or fine-tuning of the pre-trained classifier. Through extensive experiments, we demonstrate that our proposed framework can seamlessly integrate with any existing distance-based inspection method while efficiently utilizing feature extractors of varying depths. Our scheme effectively enhances the performance of out-of-distribution detection when compared to baseline methods. In particular, MLOD-Fisher achieves superior performance in general. When trained using KNN on CIFAR10, MLOD-Fisher significantly lowers the false positive rate (FPR) from 24.09% to 7.47% on average compared to merely utilizing the features of the last layer.

cs.LG↗
arXiv2024

Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization

Exploring and mining subtle yet distinctive features between sub-categories with similar appearances is crucial for fine-grained visual categorization (FGVC). However, less effort has been devoted to assessing the quality of extracted visual representations. Intuitively, the network may struggle to capture discriminative features from low-quality samples, which leads to a significant decline in FGVC performance. To tackle this challenge, we propose a weakly supervised Context-Semantic Quality Awareness Network (CSQA-Net) for FGVC. In this network, to model the spatial contextual relationship between rich part descriptors and global semantics for capturing more discriminative details within the object, we design a novel multi-part and multi-scale cross-attention (MPMSCA) module. Before feeding to the MPMSCA module, the part navigator is developed to address the scale confusion problems and accurately identify the local distinctive regions. Furthermore, we propose a generic multi-level semantic quality evaluation module (MLSQE) to progressively supervise and enhance hierarchical semantics from different levels of the backbone network. Finally, context-aware features from MPMSCA and semantically enhanced features from MLSQE are fed into the corresponding quality probing classifiers to evaluate their quality in real-time, thus boosting the discriminability of feature representations. Comprehensive experiments on four popular and highly competitive FGVC datasets demonstrate the superiority of the proposed CSQA-Net in comparison with the state-of-the-art methods.

cs.CV↗
arXiv2019

Torsional refrigeration by twisted, coiled, and supercoiled fibers

Higher efficiency, lower cost refrigeration is needed for both large and small scale cooling. Refrigerators using entropy changes during cycles of stretching or hydrostatically compression of a solid are possible alternatives to the vapor-compression fridges found in homes. We show that high cooling results from twist changes for twisted, coiled, or supercoiled fibers, including those of natural rubber, NiTi, and polyethylene fishing line. By using opposite chiralities of twist and coiling, supercoiled natural rubber fibers and coiled fishing line fibers result that cool when stretched. A demonstrated twist-based device for cooling flowing water provides a high cooling energy and device efficiency. Theory describes the axial and spring index dependencies of twist-enhanced cooling and its origin in a phase transformation for polyethylene fibers.

physics.app-ph↗