arXiv subjects

Jianfeng Li

Publications and source records attributed to Jianfeng Li.

At least 19 recordsLinked to original sources

arXiv2026

Autonomous Navigation System for Library Service Robot Based on Unitree Go2 Edu

Libraries require autonomous robots to move quietly through narrow aisles while remaining safe around readers, chairs, bags, and carts. This paper presents a ROS 2 navigation system for a Unitree Go2 Edu quadruped equipped with a 4D LiDAR, a front depth camera, and an IMU. Rather than assuming the library is rough terrain, we target the practical mobility discontinuities of real deployments, including floor transitions, temporary clutter, and partially blocked passages where low-clearance wheeled platforms are less tolerant. RTAB-Map is used for visual-LiDAR SLAM, AMCL and EKF-based sensor fusion provide localization, and a Nav2 stack with A* and DWA supports planning and local avoidance. In a real library, the system achieves 100%, 96%, and 88% success rates in static, low-density dynamic, and high-density dynamic scenes, while map validation against surveyed control distances yields a mean metric error of 3.7 cm.

cs.RO↗

arXiv2026

PILIR: Physics-Informed Local Implicit Representation

Physics-Informed Neural Networks have become a powerful mesh-free method for solving partial differential equations, but their performance is often limited by spectral bias. Specifically, in standard MLPs used in PINNs, the global parameter coupling causes the model to prioritize learning low-frequency components, resulting in slow convergence for high-frequency details. To overcome this limitation, we introduce the Physics-Informed Local Implicit Representation (PILIR). Our approach separates the global physical domain into a discrete latent feature space and a continuous generative decoder. By using a learnable grid to encode explicit spatial locality, PILIR can capture high-frequency details locally, preventing dilution by global patterns. A generative neural operator then synthesizes these local latent features into continuous physical fields, allowing accurate reconstruction of fine-scale structures. Experiments on a range of challenging PDEs show that PILIR effectively mitigates spectral bias, thereby boosting the convergence of high-frequency details and achieving superior accuracy compared to state-of-the-art methods.

cs.LG↗

arXiv2025

Dual-Projection Fusion for Accurate Upright Panorama Generation in Robotic Vision

Panoramic cameras, capable of capturing a 360-degree field of view, are crucial in robotic vision, particularly in environments with sparse features. However, non-upright panoramas due to unstable robot postures hinder downstream tasks. Traditional IMU-based correction methods suffer from drift and external disturbances, while vision-based approaches offer a promising alternative. This study presents a dual-stream angle-aware generation network that jointly estimates camera inclination angles and reconstructs upright panoramic images. The network comprises a CNN branch that extracts local geometric structures from equirectangular projections and a ViT branch that captures global contextual cues from cubemap projections. These are integrated through a dual-projection adaptive fusion module that aligns spatial features across both domains. To further enhance performance, we introduce a high-frequency enhancement block, circular padding, and channel attention mechanisms to preserve 360{\deg} continuity and improve geometric sensitivity. Experiments on the SUN360 and M3D datasets demonstrate that our method outperforms existing approaches in both inclination estimation and upright panorama generation. Ablation studies further validate the contribution of each module and highlight the synergy between the two tasks. The code and related datasets can be found at: https://github.com/YuhaoShine/DualProjectionFusion.

cs.CV↗

arXiv2025

Bizard: A Community-Driven Platform for Accelerating and Enhancing Biomedical Data Visualization

Biomedical research increasingly relies on heterogeneous, high-dimensional datasets, yet effective visualization remains hindered by fragmented code resources, steep programming barriers, and limited domain-specific guidance. Bizard is an open-source visualization code repository engineered to streamline data analysis in biomedical research. It aggregates a diverse array of executable visualization scripts, empowering researchers to select and tailor optimal graphical methods for their specific investigative demands. The platform features an intuitive interface equipped with sophisticated browsing and filtering capabilities, exhaustive tutorials, and interactive discussion forums that foster knowledge dissemination. Through its community-driven paradigm, Bizard promotes continual refinement and functional expansion, establishing itself as an essential resource for elevating biomedical data visualization and analytical standards. By harnessing Bizard's infrastructure, researchers can augment their visualization proficiency, propel methodological progress, and enhance interpretive rigor, ultimately accelerating precision medicine and personalized therapeutics. Bizard is freely accessible at https://openbiox.github.io/Bizard/.

q-bio.GN↗

arXiv2025

Fully automated inverse co-optimization of templates and block copolymer blending recipes for DSA lithography

The directed self-assembly (DSA) of block copolymers (BCPs) offers a highly promising approach for the fabrication of contact holes or vertical interconnect access at sub-7nm technology nodes. To fabricate circular holes with precisely controlled size and positions, the self-assembly of block copolymers requires guidance from a properly designed template. Effectively parameterizing the template shape to enable efficient optimization remains a critical yet challenging problem. Moreover, the optimized template must possess excellent manufacturability for practical applications. In this work, we propose a Gaussian descriptor for characterizing the template shape with only two parameters. We further propose to use AB/AB binary blends instead of pure diblock copolymer to improve the adaptability of the block copolymer system to the template shape. The Bayesian optimization (BO) is applied to co-optimize the binary blend and the template shape. Our results demonstrate that BO based on the Gaussian descriptor can efficiently yield the optimal templates for diverse multi-hole patterns, all leading to highly matched self-assembled morphologies. Moreover, by imposing constraints on the variation of curvature of the template during optimization, superior manufacturability is ensured for each optimized template. It is noteworthy that each key parameter of the blend exhibits a relatively wide tunable window under the requirement of rather high precision. Our work provides valuable insights for advancing DSA technology, and thus potentially propels its practical applications forward.

physics.comp-ph↗

arXiv2025

360-Degree Full-view Image Segmentation by Spherical Convolution compatible with Large-scale Planar Pre-trained Models

Due to the current lack of large-scale datasets at the million-scale level, tasks involving panoramic images predominantly rely on existing two-dimensional pre-trained image benchmark models as backbone networks. However, these networks are not equipped to recognize the distortions and discontinuities inherent in panoramic images, which adversely affects their performance in such tasks. In this paper, we introduce a novel spherical sampling method for panoramic images that enables the direct utilization of existing pre-trained models developed for two-dimensional images. Our method employs spherical discrete sampling based on the weights of the pre-trained models, effectively mitigating distortions while achieving favorable initial training values. Additionally, we apply the proposed sampling method to panoramic image segmentation, utilizing features obtained from the spherical model as masks for specific channel attentions, which yields commendable results on commonly used indoor datasets, Stanford2D3D.

cs.CV↗

arXiv2025

Automated Retrosynthesis Planning of Macromolecules Using Large Language Models and Knowledge Graphs

Identifying reliable synthesis pathways in materials chemistry is a complex task, particularly in polymer science, due to the intricate and often non-unique nomenclature of macromolecules. To address this challenge, we propose an agent system that integrates large language models (LLMs) and knowledge graphs. By leveraging LLMs' powerful capabilities for extracting and recognizing chemical substance names, and storing the extracted data in a structured knowledge graph, our system fully automates the retrieval of relevant literatures, extraction of reaction data, database querying, construction of retrosynthetic pathway trees, further expansion through the retrieval of additional literature and recommendation of optimal reaction pathways. By considering the complex interdependencies among chemical reactants, a novel Multi-branched Reaction Pathway Search Algorithm (MBRPS) is proposed to help identify all valid multi-branched reaction pathways, which arise when a single product decomposes into multiple reaction intermediates. In contrast, previous studies were limited to cases where a product decomposes into at most one reaction intermediate. This work represents the first attempt to develop a fully automated retrosynthesis planning agent tailored specially for macromolecules powered by LLMs. Applied to polyimide synthesis, our new approach constructs a retrosynthetic pathway tree with hundreds of pathways and recommends optimized routes, including both known and novel pathways. This demonstrates utilizing LLMs for literature consultation to accomplish specific tasks is possible and crucial for future materials research, given the vast amount of materials-related literature.

cs.AI↗

arXiv2025

The establishment of static digital humans and the integration with spinal models

Adolescent idiopathic scoliosis (AIS), a prevalent spinal deformity, significantly affects individuals' health and quality of life. Conventional imaging techniques, such as X - rays, computed tomography (CT), and magnetic resonance imaging (MRI), offer static views of the spine. However, they are restricted in capturing the dynamic changes of the spine and its interactions with overall body motion. Therefore, developing new techniques to address these limitations has become extremely important. Dynamic digital human modeling represents a major breakthrough in digital medicine. It enables a three - dimensional (3D) view of the spine as it changes during daily activities, assisting clinicians in detecting deformities that might be missed in static imaging. Although dynamic modeling holds great potential, constructing an accurate static digital human model is a crucial initial step for high - precision simulations. In this study, our focus is on constructing an accurate static digital human model integrating the spine, which is vital for subsequent dynamic digital human research on AIS. First, we generate human point - cloud data by combining the 3D Gaussian method with the Skinned Multi - Person Linear (SMPL) model from the patient's multi - view images. Then, we fit a standard skeletal model to the generated human model. Next, we align the real spine model reconstructed from CT images with the standard skeletal model. We validated the resulting personalized spine model using X - ray data from six AIS patients, with Cobb angles (used to measure the severity of scoliosis) as evaluation metrics. The results indicate that the model's error was within 1 degree of the actual measurements. This study presents an important method for constructing digital humans.

eess.IV↗

arXiv2025

Toward Automated Simulation Research Workflow through LLM Prompt Engineering Design

The advent of Large Language Models (LLMs) has created new opportunities for the automation of scientific research spanning both experimental processes and computational simulations. This study explores the feasibility of constructing an autonomous simulation agent (ASA) powered by LLMs through prompt engineering and automated program design to automate the entire simulation research process according to a human-provided research plan. This process includes experimental design, remote upload and simulation execution, data analysis, and report compilation. Using a well-studied simulation problem of polymer chain conformations as a test case, we assessed the long-task completion and reliability of ASAs powered by different LLMs, including GPT-4o, Claude-3.5, etc. Our findings revealed that ASA-GPT-4o achieved near-flawless execution on designated research missions, underscoring the potential of methods like ASA to achieve automation in simulation research processes to enhance research efficiency. The outlined automation can be iteratively performed for up to 20 cycles without human intervention, illustrating the potential of ASA for long-task workflow automation. Additionally, we discussed the intrinsic traits of ASA in managing extensive tasks, focusing on self-validation mechanisms, and the balance between local attention and global oversight.

cs.AI↗

arXiv2024

Learning to Adapt to Low-Resource Paraphrase Generation

Paraphrase generation is a longstanding NLP task and achieves great success with the aid of large corpora. However, transferring a paraphrasing model to another domain encounters the problem of domain shifting especially when the data is sparse. At the same time, widely using large pre-trained language models (PLMs) faces the overfitting problem when training on scarce labeled data. To mitigate these two issues, we propose, LAPA, an effective adapter for PLMs optimized by meta-learning. LAPA has three-stage training on three types of related resources to solve this problem: 1. pre-training PLMs on unsupervised corpora, 2. inserting an adapter layer and meta-training on source domain labeled data, and 3. fine-tuning adapters on a small amount of target domain labeled data. This method enables paraphrase generation models to learn basic language knowledge first, then learn the paraphrasing task itself later, and finally adapt to the target task. Our experimental results demonstrate that LAPA achieves state-of-the-art in supervised, unsupervised, and low-resource settings on three benchmark datasets. With only 2\% of trainable parameters and 1\% labeled data of the target task, our approach can achieve a competitive performance with previous work.

cs.CL↗

arXiv2024

HEP-NAS: Towards Efficient Few-shot Neural Architecture Search via Hierarchical Edge Partitioning

One-shot methods have significantly advanced the field of neural architecture search (NAS) by adopting weight-sharing strategy to reduce search costs. However, the accuracy of performance estimation can be compromised by co-adaptation. Few-shot methods divide the entire supernet into individual sub-supernets by splitting edge by edge to alleviate this issue, yet neglect relationships among edges and result in performance degradation on huge search space. In this paper, we introduce HEP-NAS, a hierarchy-wise partition algorithm designed to further enhance accuracy. To begin with, HEP-NAS treats edges sharing the same end node as a hierarchy, permuting and splitting edges within the same hierarchy to directly search for the optimal operation combination for each intermediate node. This approach aligns more closely with the ultimate goal of NAS. Furthermore, HEP-NAS selects the most promising sub-supernet after each segmentation, progressively narrowing the search space in which the optimal architecture may exist. To improve performance evaluation of sub-supernets, HEP-NAS employs search space mutual distillation, stabilizing the training process and accelerating the convergence of each individual sub-supernet. Within a given budget, HEP-NAS enables the splitting of all edges and gradually searches for architectures with higher accuracy. Experimental results across various datasets and search spaces demonstrate the superiority of HEP-NAS compared to state-of-the-art methods.

cs.LG↗

arXiv2024

Switchable deep beamformer for high-quality and real-time passive acoustic mapping

Passive acoustic mapping (PAM) is a promising tool for monitoring acoustic cavitation activities in the applications of ultrasound therapy. Data-adaptive beamformers for PAM have better image quality compared to the time exposure acoustics (TEA) algorithms. However, the computational cost of data-adaptive beamformers is considerably expensive. In this work, we develop a deep beamformer based on a generative adversarial network, which can switch between different transducer arrays and reconstruct high-quality PAM images directly from radio frequency ultrasound signals with low computational cost. The deep beamformer was trained on the dataset consisting of simulated and experimental cavitation signals of single and multiple microbubble clouds measured by different (linear and phased) arrays covering 1-15 MHz. We compared the performance of the deep beamformer to TEA and three different data-adaptive beamformers using the simulated and experimental test dataset. Compared with TEA, the deep beamformer reduced the energy spread area by 18.9%-65.0% and improved the image signal-to-noise ratio by 9.3-22.9 dB in average for the different arrays in our data. Compared to the data-adaptive beamformers, the deep beamformer reduced the computational cost by three orders of magnitude achieving 10.5 ms image reconstruction speed in our data, while the image quality was as good as that of the data-adaptive beamformers. These results demonstrated the potential of the deep beamformer for high-resolution monitoring of microbubble cavitation activities for ultrasound therapy.

cs.AI↗

arXiv2024

High-Quality Passive Acoustic Mapping with the Cross-Correlated Angular Spectrum Method

While passive acoustic mapping (PAM) has been advanced for monitoring acoustic cavitation activity in focused ultrasound (FUS) therapy, achieving both real-time and high-quality imaging capabilities is still challenging. The angular spectrum (AS) method presents the most efficient algorithm for PAM, but it suffers from artifacts and low resolution due to the diffraction pattern of the imaging array. Data-adaptive beamformers suppress artifacts well, but their overwhelming computational complexity, more than two orders of magnitude higher than the classical time exposure acoustic (TEA) method, hinders their application in real-time. In this work, we introduce the cross-correlated AS method to address the challenge. This method is based on cross-correlating the AS back-propagated wave fields, in the frequency domain, measured by different apodized sub-apertures of the transducer array to provide the normalized correlation coefficient (NCC) matrix for artifacts suppression. We observed that the spatial pattern of NCC matrix is variable which can be utilized by the triple apodization with cross-correlation (TAX) with AS scheme, namely the AS-TAX method, for optimal artifacts suppression outcomes. Both the phantom and mouse tumor experiments showed that: 1) the AS-TAX method has comparable image quality as the data-adaptive beamformers, reducing the energy spread area by 34.8-66.6% and improving image signal-to-noise ratio by 10.7-14.5 dB compared to TEA; 2) it reduces the computational complexity by two orders of magnitude compared to TEA allowing millisecond-level image reconstruction speed with a parallel implementation; 3) it can well map microbubble cavitation activity of different status (stable or inertial). The AS-TAX method represents a real-time approach to monitor cavitation-based FUS therapy with high image quality.

physics.med-ph↗

arXiv2024

ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents

Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of controllability** remains a key challenge, often leading to unfocused conversations or task failure. To address this, we introduce Standard Operating Procedure (SOP) to regulate dialogue flow. Specifically, we propose **ChatSOP**, a novel SOP-guided Monte Carlo Tree Search (MCTS) planning framework designed to enhance the controllability of LLM-driven dialogue agents. To enable this, we curate a dataset comprising SOP-annotated multi-scenario dialogues, generated using a semi-automated role-playing system with GPT-4o and validated through strict manual quality control. Additionally, we propose a novel method that integrates Chain of Thought reasoning with supervised fine-tuning for SOP prediction and utilizes SOP-guided Monte Carlo Tree Search for optimal action planning during dialogues. Experimental results demonstrate the effectiveness of our method, such as achieving a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also showing notable gains for open-source models. Dataset and codes are publicly available.

cs.CL↗

arXiv2024

Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations

Disconnectivity and distortion are the two problems which must be coped with when processing 360 degrees equirectangular images. In this paper, we propose a method of estimating the depth of monocular panoramic image with a teacher-student model fusing equirectangular and spherical representations. In contrast with the existing methods fusing an equirectangular representation with a cube map representation or tangent representation, a spherical representation is a better choice because a sampling on a sphere is more uniform and can also cope with distortion more effectively. In this processing, a novel spherical convolution kernel computing with sampling points on a sphere is developed to extract features from the spherical representation, and then, a Segmentation Feature Fusion(SFF) methodology is utilized to combine the features with ones extracted from the equirectangular representation. In contrast with the existing methods using a teacher-student model to obtain a lighter model of depth estimation, we use a teacher-student model to learn the latent features of depth images. This results in a trained model which estimates the depth map of an equirectangular image using not only the feature maps extracted from an input equirectangular image but also the distilled knowledge learnt from the ground truth of depth map of a training set. In experiments, the proposed method is tested on several well-known 360 monocular depth estimation benchmark datasets, and outperforms the existing methods for the most evaluation indexes.

cs.CV↗

arXiv2024

ABC-Channel: An Advanced Blockchain-based Covert Channel

Establishing efficient and robust covert channels is crucial for secure communication within insecure network environments. With its inherent benefits of decentralization and anonymization, blockchain has gained considerable attention in developing covert channels. To guarantee a highly secure covert channel, channel negotiation should be contactless before the communication, carrier transaction features must be indistinguishable from normal transactions during the communication, and communication identities must be untraceable after the communication. Such a full-lifecycle covert channel is indispensable to defend against a versatile adversary who intercepts two communicating parties comprehensively (e.g., on-chain and off-chain). Unfortunately, it has not been thoroughly investigated in the literature. We make the first effort to achieve a full-lifecycle covert channel, a novel blockchain-based covert channel named ABC-Channel. We tackle a series of challenges, such as off-chain contact dependency, increased masquerading difficulties as growing transaction volume, and time-evolving, communicable yet untraceable identities, to achieve contactless channel negotiation, indistinguishable transaction features, and untraceable communication identities, respectively. We develop a working prototype to validate ABC-Channel and conduct extensive tests on the Bitcoin testnet. The experimental results demonstrate that ABC-Channel achieves substantially secure covert capabilities. In comparison to existing methods, it also exhibits state-of-the-art transmission efficiency.

cs.CR↗

arXiv2024

TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation

Over the years, network traffic analysis and generation have advanced significantly. From traditional statistical methods, the field has progressed to sophisticated deep learning techniques. This progress has improved the ability to detect complex patterns and security threats, as well as to test and optimize network performance. However, obstacles persist, such as the dependence on labeled data for analysis and the difficulty of generating traffic samples that follow realistic patterns. Pre-trained deep neural networks have emerged as powerful tools to resolve these issues, offering improved performance by learning robust data representations from large unlabeled datasets. Despite their benefits, existing pre-trained models face challenges like token length limitation, which restricts their usefulness in comprehensive traffic analysis and realistic traffic generation. To address these challenges, we introduce TrafficGPT, a deep learning model that can tackle complex challenges related to long flow classification and generation tasks. This model uses generative pre-training with the linear attention mechanism, which allows for a substantially increased capacity of up to 12,032 tokens from the previous limit of only 512 tokens. TrafficGPT demonstrates superior performance in classification tasks, reaching state-of-the-art levels. In generation tasks, it closely resembles real traffic flows, with low JS divergence and an F1 score close to 0.5 (representing a random guess) in discriminating generated data. These advancements hold promise for future applications in both traffic flow classification and generation tasks.

cs.LG↗

arXiv2024

Low-cost and Convenient Fabrication of Polymer Micro/Nanopores with the Needle Punching Process and Their Applications in Nanofluidic Sensing

Solid-state micro/nanopores play an important role in the sensing field because of their high stability and controllable size. Aiming at problems of complex processes and high costs in pore manufacturing, we propose a convenient and low-cost micro/nanopore fabrication technique based on the needle punching method. The thin film is pierced by controlling the feed of a microscale tungsten needle, and the size variations of the micropore are monitored by the current feedback system. Based on the positive correlation between the micropore size and the current threshold, the size-controllable preparation of micropores is achieved. The preparation of nanopores is realized by the combination of needle punching and chemical etching. Firstly, a conical defect is prepared on the film with the tungsten needle. Then, nanopores are obtained by unilateral chemical etching of the film. Using the prepared conical micropores resistive-pulse detection of nanoparticles is performed. Significant ionic current rectification is also obtained with our conical nanopores. It is proved that the properties of micro/nanopores prepared by our method are comparable to those prepared by the track-etching method. The simple and controllable fabrication process proposed here will advance the development of low-cost micro/nanopore sensors.

physics.chem-ph↗