arXiv subjects

Jun Huang

Publications and source records attributed to Jun Huang.

At least 91 records · Page 5Linked to original sources

arXiv2025

AirVista-II: An Agentic System for Embodied UAVs Toward Dynamic Scene Semantic Understanding

Unmanned Aerial Vehicles (UAVs) are increasingly important in dynamic environments such as logistics transportation and disaster response. However, current tasks often rely on human operators to monitor aerial videos and make operational decisions. This mode of human-machine collaboration suffers from significant limitations in efficiency and adaptability. In this paper, we present AirVista-II -- an end-to-end agentic system for embodied UAVs, designed to enable general-purpose semantic understanding and reasoning in dynamic scenes. The system integrates agent-based task identification and scheduling, multimodal perception mechanisms, and differentiated keyframe extraction strategies tailored for various temporal scenarios, enabling the efficient capture of critical scene information. Experimental results demonstrate that the proposed system achieves high-quality semantic understanding across diverse UAV-based dynamic scenarios under a zero-shot setting.

cs.RO↗

arXiv2025

A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions

Recently, the reasoning capabilities of large reasoning models (LRMs), such as DeepSeek-R1, have seen significant advancements through the slow thinking process. Despite these achievements, the substantial computational demands of LRMs present considerable challenges. In contrast, small reasoning models (SRMs), often distilled from larger ones, offer greater efficiency and can exhibit distinct capabilities and cognitive trajectories compared to LRMs. This work surveys around 170 recently published papers on SRMs for tackling various complex reasoning tasks. We review the current landscape of SRMs and analyze diverse training and inference techniques related to SRMs. Furthermore, we provide a comprehensive review of SRMs for domain-specific applications and discuss possible future research directions. This survey serves as an essential reference for researchers to leverage or develop SRMs for advanced reasoning functionalities with high efficiency.

cs.AI↗

arXiv2025

UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility

Low-altitude mobility, exemplified by unmanned aerial vehicles (UAVs), has introduced transformative advancements across various domains, like transportation, logistics, and agriculture. Leveraging flexible perspectives and rapid maneuverability, UAVs extend traditional systems' perception and action capabilities, garnering widespread attention from academia and industry. However, current UAV operations primarily depend on human control, with only limited autonomy in simple scenarios, and lack the intelligence and adaptability needed for more complex environments and tasks. The emergence of large language models (LLMs) demonstrates remarkable problem-solving and generalization capabilities, offering a promising pathway for advancing UAV intelligence. This paper explores the integration of LLMs and UAVs, beginning with an overview of UAV systems' fundamental components and functionalities, followed by an overview of the state-of-the-art in LLM technology. Subsequently, it systematically highlights the multimodal data resources available for UAVs, which provide critical support for training and evaluation. Furthermore, it categorizes and analyzes key tasks and application scenarios where UAVs and LLMs converge. Finally, a reference roadmap towards agentic UAVs is proposed, aiming to enable UAVs to achieve agentic intelligence through autonomous perception, memory, reasoning, and tool utilization. Related resources are available at https://github.com/Hub-Tian/UAVs_Meet_LLMs.

cs.RO↗

arXiv2025

Spatiotemporal Impact of Trade Policy Variables on Asian Manufacturing Hubs: Bayesian Global Vector Autoregression Model

A novel spatiotemporal framework using diverse econometric approaches is proposed in this research to analyze relationships among eight economy-wide variables in varying market conditions. Employing Vector Autoregression (VAR) and Granger causality, we explore trade policy effects on emerging manufacturing hubs in China, India, Malaysia, Singapore, and Vietnam. A Bayesian Global Vector Autoregression (BGVAR) model also assesses interaction of cross unit and perform Unconditional and Conditional Forecasts. Utilizing time-series data from the Asian Development Bank, our study reveals multi-way cointegration and dynamic connectedness relationships among key economy-wide variables. This innovative framework enhances investment decisions and policymaking through a data-driven approach.

econ.EM↗

arXiv2025

DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding

Text-rich document understanding (TDU) requires comprehensive analysis of documents containing substantial textual content and complex layouts. While Multimodal Large Language Models (MLLMs) have achieved fast progress in this domain, existing approaches either demand significant computational resources or struggle with effective multi-modal integration. In this paper, we introduce DocLayLLM, an efficient multi-modal extension of LLMs specifically designed for TDU. By lightly integrating visual patch tokens and 2D positional tokens into LLMs' input and encoding the document content using the LLMs themselves, we fully take advantage of the document comprehension capability of LLMs and enhance their perception of OCR information. We have also deeply considered the role of chain-of-thought (CoT) and innovatively proposed the techniques of CoT Pre-training and CoT Annealing. Our DocLayLLM can achieve remarkable performances with lightweight training settings, showcasing its efficiency and effectiveness. Experimental results demonstrate that our DocLayLLM outperforms existing OCR-dependent methods and OCR-free competitors. Code and model are available at https://github.com/whlscut/DocLayLLM.

cs.CV↗

arXiv2025

Beyond Existance: Fulfill 3D Reconstructed Scenes with Pseudo Details

The emergence of 3D Gaussian Splatting (3D-GS) has significantly advanced 3D reconstruction by providing high fidelity and fast training speeds across various scenarios. While recent efforts have mainly focused on improving model structures to compress data volume or reduce artifacts during zoom-in and zoom-out operations, they often overlook an underlying issue: training sampling deficiency. In zoomed-in views, Gaussian primitives can appear unregulated and distorted due to their dilation limitations and the insufficient availability of scale-specific training samples. Consequently, incorporating pseudo-details that ensure the completeness and alignment of the scene becomes essential. In this paper, we introduce a new training method that integrates diffusion models and multi-scale training using pseudo-ground-truth data. This approach not only notably mitigates the dilation and zoomed-in artifacts but also enriches reconstructed scenes with precise details out of existing scenarios. Our method achieves state-of-the-art performance across various benchmarks and extends the capabilities of 3D reconstruction beyond training datasets.

cs.GR↗

arXiv2025

DCI: A Coordinated Allocation and Filling Workload-Aware Dual-Cache Allocation GNN Inference Acceleration System

Graph Neural Networks (GNNs) are powerful tools for processing graph-structured data, increasingly used for large-scale real-world graphs via sampling-based inference methods. However, inherent characteristics of neighbor sampling lead to redundant data loading during GNN inference, compounded by inefficient data transfers between host and GPU memory, resulting in slow inference and low resource utilization. Existing methods to accelerate GNN inference face several challenges: (1) low practical GPU memory utilization, (2) overlooking adjacency matrix locality, and (3) long preprocessing time. To address these challenges, we introduce DCI, an efficient workload-aware dual-cache allocation system for GNN inference acceleration. DCI allocates cache capacities for both node features and adjacency matrices based on workload patterns during the pre-sampling phase, leveraging a lightweight cache-filling algorithm to optimize data loading efficiency. Experimental results demonstrate that DCI accelerates sampling and node feature loading, achieving end-to-end inference speedups of 1.18$\times$ to 11.26$\times$ compared to DGL, and 1.14$\times$ to 13.68$\times$ over RAIN, while reducing preprocessing time by 52.8\% to 98.7\%. Additionally, DCI outperforms state-of-the-art single-cache inference systems by achieving speedup of 1.08$\times$ to 1.32$\times$. We also compared DCI with DUCATI's dual-cache population strategy. Our lightweight population algorithm allows DCI to achieve nearly the same inference speed while keeping preprocessing time to less than 20\% of that required by DUCATI.

cs.AR↗

arXiv2025

Theory of Electro-Ionic Perturbations at Supported Electrocatalyst Nanoparticles

Nanoscopic heterogeneities in composition and structure are quintessential for the properties of electrocatalyst materials. Here, we present a semiclassical model to study the electrochemical properties of supported electrocatalyst nanoparticles (NP). The model captures the correlated electronic and ionic equilibration across NP, support, and electrolyte. It reveals peculiar trends in surface charging of the supported NP, validated by comparison with first-principles calculations. Support-induced perturbations in electronic and ionic charge densities at the NP's active surface manifest as distinct potentials of zero local electronic and ionic charges that could differ by more than 0.5 V in the studied system.

cond-mat.mtrl-sci↗

arXiv2025

On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook

Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life. However, the practical implementations of these models have also exposed inherent risks, bringing to the forefront their evil sides and sparking concerns regarding their trustworthiness. Despite the wealth of literature on this subject, a comprehensive survey specifically delving into the intersection of large-scale generative models and their trustworthiness remains largely absent. To bridge this gap, this paper investigates both the long-standing and emerging threats associated with these models across four fundamental dimensions: 1) privacy, 2) security, 3) fairness, and 4) responsibility. Based on the investigation results, we develop an extensive map outlining the trustworthiness of large generative models. After that, we provide practical recommendations and potential research directions for future secure applications equipped with large generative models, ultimately promoting the trustworthiness of the models and benefiting the society as a whole.

cs.LG↗

arXiv2025

A fluorescent-protein spin qubit

Optically-addressable spin qubits form the foundation of a new generation of emerging nanoscale sensors. The engineering of these sensors has mainly focused on solid-state systems such as the nitrogen-vacancy (NV) center in diamond. However, NVs are restricted in their ability to interface with biomolecules due to their bulky diamond host. Meanwhile, fluorescent proteins have become the gold standard in bioimaging, as they are genetically encodable and easily integrated with biomolecules. While fluorescent proteins have been suggested to possess a metastable triplet state, they have not been investigated as qubit sensors. Here, we realize an optically-addressable spin qubit in the Enhanced Yellow Fluorescent Protein (EYFP) enabled by a novel spin-readout technique. A near-infrared laser pulse allows for triggered readout of the triplet state with up to 44% spin contrast. Using coherent microwave control of the EYFP spin at liquid-nitrogen temperatures, we measure a spin-lattice relaxation time of $(141 \pm 5)\, \mathrm{μs}$, a $(16 \pm 2)\, \mathrm{μs}$ coherence time under Carr-Purcell-Meiboom-Gill (CPMG) decoupling, and a predicted oscillating (AC) magnetic field sensitivity with an upper bound of $183 \, \mathrm{fT}\, \mathrm{mol}^{1/2}\, \mathrm{Hz}^{-1/2}$. We express the qubit in mammalian cells, maintaining contrast and coherent control despite the complex intracellular environment. Finally, we demonstrate optically-detected magnetic resonance at room temperature in aqueous solution with contrast up to 3%, and measure a static (DC) field sensitivity with an upper bound of $93 \, \mathrm{pT}\, \mathrm{mol}^{1/2}\, \mathrm{Hz}^{-1/2}$. Our results establish fluorescent proteins as a powerful new qubit sensor platform and pave the way for applications in the life sciences that are out of reach for solid-state technologies.

quant-ph↗

arXiv2024

Formation of Fe-6.5wt%Si High Silicon Steel by Double Glow Plasma Surface Metallurgy Technology

High silicon steel with 6.5% silicon content is the best because of its excellent magnetic properties, such as high saturation magnetization, high resistivity, low iron loss and near zero magnetostriction. High silicon steel can greatly save energy, and reduce the weight and size of electrical appliances. This has a very important application prospect for energy and aerospace industry. The high brittleness of high silicon steel makes its production and processing very difficult. For more than 30 years, many steel companies and research institutions around the world have adopted various technical means to study the industrialization of high silicon steel, but they have not been successful . JFE-NKK steel company in Japan has realized the small batch production of high silicon steel by using SiCl4-CVD technology. However, due to the complex process, corrosion and pollution, high cost, its production scale is greatly limited. So far, large-scale production of high silicon steel is still a major challenge in the world. This paper will introduce the experimental results of successfully preparing high silicon steel by Double Glow Plasma Surface Metallurgy Technology. The process is simple and easy without any corrosion or pollution, which may provide a new way for the world to achieve large-scale production of high silicon steel. The large-scale production and wide application of high silicon steel is likely to change the pattern of the world's energy and electric power industry, save a lot of energy for mankind, and create huge economic benefits.

physics.app-ph↗

arXiv2024

Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud

Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source models have become strong enough to handle dataset construction in many scenarios. Thus, we present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning. These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs: instruction expansion, instruction refinement, and instruction-response pair expansion. To fulfill this goal, we first construct an automatic data collection system with seed datasets generated from both public repositories and our in-house datasets. This system leverages powerful LLMs to expand, refine and re-write the instructions and responses, incorporating quality assessment techniques. Following this, we introduce the training process of our models, which effectively distills task-solving and text synthesis abilities from teacher LLMs. Finally, we demonstrate how we integrate these functionalities into a machine learning platform to support low-cost LLM fine-tuning from both dataset preparation and training perspectives for users. Experiments and an application study prove the effectiveness of our approach.

cs.CL↗

arXiv2024

Variational functional theory for coulombic correlations in the electric double layer

A classical coulombic correlation functional in one-loop (1L) and local-density-approximation (LDA) is derived for electrolyte solutions, starting from a first-principles many-body partition function. The 1L-LDA functional captures correlations between electrolyte ions and solvent dipoles, such as screening and solvation, that are ignored by conventional mean-field theories. This 1L-LDA functional introduces two parameters that can be tuned to the experimental dielectric permittivity and activity coefficients in the bulk electrolyte solution. The capabilities of the 1L-LDA functional for the description of metal-electrolyte interfaces are demonstrated by embedding the functional into a combined quantum-classical model. Here, the 1L-LDA functional leads to a more pronounced double-peak structure of the interfacial capacitance with higher peaks and shorter peak-to-peak distance, significantly improving the agreement with experimental data and showing that electrolyte correlation effects exert a vital impact on the capacitive response.

physics.chem-ph↗

arXiv2024

An Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services

This article presents the design of an open-API-based explainable AI (XAI) service to provide feature contribution explanations for cloud AI services. Cloud AI services are widely used to develop domain-specific applications with precise learning metrics. However, the underlying cloud AI services remain opaque on how the model produces the prediction. We argue that XAI operations are accessible as open APIs to enable the consolidation of the XAI operations into the cloud AI services assessment. We propose a design using a microservice architecture that offers feature contribution explanations for cloud AI services without unfolding the network structure of the cloud models. We can also utilize this architecture to evaluate the model performance and XAI consistency metrics showing cloud AI services trustworthiness. We collect provenance data from operational pipelines to enable reproducibility within the XAI service. Furthermore, we present the discovery scenarios for the experimental tests regarding model performance and XAI consistency metrics for the leading cloud vision AI services. The results confirm that the architecture, based on open APIs, is cloud-agnostic. Additionally, data augmentations result in measurable improvements in XAI consistency metrics for cloud AI services.

cs.DC↗

arXiv2024

Radical-mediated Electrical Enzyme Assay For At-home Clinical Test

To meet the growing demand for accurate, rapid, and cost-effective at-home clinical testing, we developed a radical-mediated enzyme assay (REEA) integrated with a paper fluidic system and electrically read by a handheld field-effect transistor (FET) device. The REEA utilizes horseradish peroxidase (HRP) to catalyze the conversion of aromatic substrates into radical forms, producing protons detected by an ion-sensitive FET for biomarker quantification. Through screening 14 phenolic compounds, halogenated phenols emerged as optimal substrates for the REEA. Encased in an affordable cartridge ($0.55 per test), the system achieved a detection limit of 146 fg/mL for estradiol (E2), with a coefficient of variation (CV) below 9.2% in E2-spiked samples and an r2 of 0.963 across a measuring range of 19 to 4,551 pg/mL in clinical plasma samples, providing results in under 10 minutes. This adaptable system not only promises to offer a fast and reliable platform, but also holds significant potential for expansion to a wide array of biomarkers, paving the way for broader clinical and home-based applications.

q-bio.QM↗

arXiv2024

VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models

Contrastive Language-Image Pre-training (CLIP) has been widely studied and applied in numerous applications. However, the emphasis on brief summary texts during pre-training prevents CLIP from understanding long descriptions. This issue is particularly acute regarding videos given that videos often contain abundant detailed contents. In this paper, we propose the VideoCLIP-XL (eXtra Length) model, which aims to unleash the long-description understanding capability of video CLIP models. Firstly, we establish an automatic data collection system and gather a large-scale VILD pre-training dataset with VIdeo and Long-Description pairs. Then, we propose Text-similarity-guided Primary Component Matching (TPCM) to better learn the distribution of feature space while expanding the long description capability. We also introduce two new tasks namely Detail-aware Description Ranking (DDR) and Hallucination-aware Description Ranking (HDR) for further understanding improvement. Finally, we construct a Long Video Description Ranking (LVDR) benchmark for evaluating the long-description capability more comprehensively. Extensive experimental results on widely-used text-video retrieval benchmarks with both short and long descriptions and our LVDR benchmark can fully demonstrate the effectiveness of our method.

cs.CL↗

arXiv2024

Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning

Instruction tuning aims to align large language models (LLMs) with open-domain instructions and human-preferred responses. While several studies have explored autonomous approaches to distilling and annotating instructions from powerful proprietary LLMs, such as ChatGPT, they often neglect the impact of the distributions and characteristics of tasks, together with the varying difficulty of instructions in training sets. This oversight can lead to imbalanced knowledge capabilities and poor generalization powers of student LLMs. To address these challenges, we introduce Task-Aware Curriculum Planning for Instruction Refinement (TAPIR), a multi-round distillation framework that utilizes an oracle LLM to select instructions that are difficult for a student LLM to follow. To balance the student's capabilities, task distributions in training sets are adjusted with responses automatically refined according to their corresponding tasks. In addition, by incorporating curriculum planning, our approach systematically escalates the difficulty levels of tasks, progressively enhancing the student LLM's capabilities. We rigorously evaluate TAPIR using several widely recognized benchmarks (such as AlpacaEval 2.0, MT-Bench, etc.) and multiple student LLMs. Empirical results demonstrate that student LLMs, trained with our method and less training data, outperform larger instruction-tuned models and strong distillation baselines.

cs.CL↗

arXiv2024

Rotation center identification based on geometric relationships for rotary motion deblurring

Non-blind rotary motion deblurring (RMD) aims to recover the latent clear image from a rotary motion blurred (RMB) image. The rotation center is a crucial input parameter in non-blind RMD methods. Existing methods directly estimate the rotation center from the RMB image. However they always suffer significant errors, and the performance of RMD is limited. For the assembled imaging systems, the position of the rotation center remains fixed. Leveraging this prior knowledge, we propose a geometric-based method for rotation center identification and analyze its error range. Furthermore, we construct a RMB imaging system. The experiment demonstrates that our method achieves less than 1-pixel error along a single axis (x-axis or y-axis). We utilize the constructed imaging system to capture real RMB images, and experimental results show that our method can help existing RMD approaches yield better RMD images.

cs.CV↗