arXiv subjects

Mingyue Li

Publications and source records attributed to Mingyue Li.

7 recordsLinked to original sources

arXiv2026

ChannelAgent-Empowered Electromagnetic Space World Model: A Case Study on Agent-Driven Channel Generation for 6G AI-Native Air Interface

As sixth-generation (6G) wireless networks evolve toward increasingly heterogeneous scenarios, tasks, and service requirements, conventional artificial intelligence (AI) models remain limited in task-aware decision-making and autonomous adaptation. To address this issue, this paper first proposes a ChannelAgent-empowered electromagnetic space world model, in which wireless intelligence is organized into a closed-loop process consisting of multi-modal sensing, ChannelAgent as the intelligent core, and execution with feedback update. As a case study, agent-driven channel generation is instantiated through path loss prediction. Specifically, a task-oriented intelligent feature selection mechanism is designed by integrating reinforcement-learning-inspired policy adaptation with evolutionary search, enabling the agent to iteratively derive compact and task-suitable feature subsets according to the current scenario and performance feedback. Simulation results demonstrate superior performance in both single-scenario and multi-scenario tasks, highlighting the potential of the proposed model for autonomous, adaptive, task-oriented, and closed-loop wireless intelligence.

eess.SP↗

arXiv2026

Phase-Interface Instance Segmentation as a Visual Sensor for Laboratory Process Monitoring

Reliable visual monitoring of chemical experiments remains challenging in transparent glassware, where weak phase boundaries and optical artifacts degrade conventional segmentation. We formulate laboratory phenomena as the time evolution of phase interfaces and introduce the Chemical Transparent Glasses dataset 2.0 (CTG 2.0), a vessel-aware benchmark with 3,668 images, 23 glassware categories, and five multiphase interface types for phase-interface instance segmentation. Building on YOLO11m-seg, we propose LGA-RCM-YOLO, which combines Local-Global Attention (LGA) for robust semantic representation and a Rectangular Self-Calibration Module (RCM) for boundary refinement of thin, elongated interfaces. On CTG 2.0, the proposed model achieves 84.4% AP@0.5 and 58.43% AP@0.5-0.95, improving over the YOLO11m baseline by 6.42 and 8.75 AP points, respectively, while maintaining near real-time inference (13.67 FPS, RTX 3060). An auxiliary color-attribute head further labels liquid instances as colored or colorless with 98.71% precision and 98.32% recall. Finally, we demonstrate continuous process monitoring in separatory-funnel phase separation and crystallization, showing that phase-interface instance segmentation can serve as a practical visual sensor for laboratory automation.

cs.CV↗

arXiv2025

Multi-wavelength study of the pre-eruption dip in the recurrent nova T Coronae Borealis preceding imminent nova eruption

We present a multi-wavelength study of the symbiotic recurrent nova (RN) T Coronae Borealis (T CrB) using Swift Burst Alert Telescope (BAT) / X-Ray Telescope (XRT) / UltraViolet Optical Telescope (UVOT) and American Association of Variable Stars Observers (AAVSO) observations from 2005 to 2025. Our analysis spans quiescent, high, and pre-eruption dip states. We find that brightening amplitudes increase toward shorter wavelengths in both optical and UV bands, while the UV and X-ray fluxes are generally anti-correlated throughout all phases. During the 2023-2024 pre-eruption dip, soft and hard X-rays increased as optical and ultraviolet (UV) brightness declined, consistent with a transition from an optically thick to thin boundary layer driven by a reduction in the accretion rate. We also report, for the first time, a second, lower-amplitude dip occurring between September 2024 and February 2025 following the primary 2023-2024 pre-eruption dip. The observed variability supports an accretion-variation scenario as a unifying explanation for both the high and dip states, and may signal an imminent nova eruption.

astro-ph.HE↗

arXiv2024

DDRN:a Data Distribution Reconstruction Network for Occluded Person Re-Identification

In occluded person re-identification(ReID), severe occlusions lead to a significant amount of irrelevant information that hinders the accurate identification of individuals. These irrelevant cues primarily stem from background interference and occluding interference, adversely affecting the final retrieval results. Traditional discriminative models, which rely on the specific content and positions of the images, often misclassify in cases of occlusion. To address these limitations, we propose the Data Distribution Reconstruction Network (DDRN), a generative model that leverages data distribution to filter out irrelevant details, enhancing overall feature perception ability and reducing irrelevant feature interference. Additionally, severe occlusions lead to the complexity of the feature space. To effectively handle this, we design a multi-center approach through the proposed Hierarchical SubcenterArcface (HS-Arcface) loss function, which can better approximate complex feature spaces. On the Occluded-Duke dataset, we achieved a mAP of 62.4\% (+1.1\%) and a rank-1 accuracy of 71.3\% (+0.6\%), surpassing the latest state-of-the-art methods(FRT) significantly.

cs.CV↗

arXiv2024

Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation

Using 286 research papers collected from Web of Science, ScienceDirect, SpringerLink, arXiv, and Google Scholar databases, a systematic review methodology was adopted to review and summarize the current challenges and potential future developments in data, algorithms, and evaluation aspects of RSs. It was found that RSs involve five major research topics, namely algorithmic improvement, domain applications, user behavior & cognition, data processing & modeling, and social impact & ethics. Collaborative filtering and hybrid recommendation techniques are mainstream. The performance of RSs is jointly limited by four types of eight data issues, two types of twelve algorithmic issues, and two evaluation issues. Notably, data-related issues such as cold start, data sparsity, and data poisoning, algorithmic issues like interest drift, device-cloud collaboration, non-causal driven, and multitask conflicts, along with evaluation issues such as offline data leakage and multi-objective balancing, have prominent impacts. Fusing physiological signals for multimodal modeling, defending against data poisoning through user information behavior, evaluating generative recommendations via social experiments, fine-tuning pre-trained large models to schedule device-cloud resource, enhancing causal inference with deep reinforcement learning, training multi-task models based on probability distributions, using cross-temporal dataset partitioning, and evaluating recommendation objectives across the full lifecycle are feasible solutions to address the aforementioned prominent challenges and unlock the power and value of RSs.The collected literature is mainly based on major international databases, and future research will further expand upon it.

cs.IR↗

arXiv2023

Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian Process

Existing deraining methods focus mainly on a single input image. However, with just a single input image, it is extremely difficult to accurately detect and remove rain streaks, in order to restore a rain-free image. In contrast, a light field image (LFI) embeds abundant 3D structure and texture information of the target scene by recording the direction and position of each incident ray via a plenoptic camera. LFIs are becoming popular in the computer vision and graphics communities. However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem. In this paper, we propose a novel method, 4D-MGP-SRRNet, for rain streak removal from LFIs. Our method takes as input all sub-views of a rainy LFI. To make full use of the LFI, it adopts 4D convolutional layers to simultaneously process all sub-views of the LFI. In the pipeline, the rain detection network, MGPDNet, with a novel Multi-scale Self-guided Gaussian Process (MSGP) module is proposed to detect high-resolution rain streaks from all sub-views of the input LFI at multi-scales. Semi-supervised learning is introduced for MSGP to accurately detect rain streaks by training on both virtual-world rainy LFIs and real-world rainy LFIs at multi-scales via computing pseudo ground truths for real-world rain streaks. We then feed all sub-views subtracting the predicted rain streaks into a 4D convolution-based Depth Estimation Residual Network (DERNet) to estimate the depth maps, which are later converted into fog maps. Finally, all sub-views concatenated with the corresponding rain streaks and fog maps are fed into a powerful rainy LFI restoring model based on the adversarial recurrent neural network to progressively eliminate rain streaks and recover the rain-free LFI.

cs.CV↗

arXiv2019

Spatial Variation of the Chemical Properties of Massive Star-forming Clumps

We selected 90 massive star-forming clumps with strong N2H+, HCO+, HCN, and HNC emission from the Millimetre Astronomy Legacy Team 90 GHz survey. We obtained Herschel data for all 90 sources and NRAO VLA Sky Survey data for 51 of them. We convolved and regridded all images to the same resolution and pixel size and derived the temperature, H2 column density, molecules' abundances and abundance, and ratios of each pixel. Our analysis yields three main conclusions. First, the abundances of N2H+, HCO+, HCN, and HNC increase when the column density decreases and the temperature increases, with spatial variations in their abundances dominated by changes in the H2 column density. Second, the abundance ratios between N2H+, HCO+, HCN, and HNC also display systemic variations as a function of the column density due to the chemical properties of these molecules. Third, the sources associated with the 20 cm continuum emission can be classified into four types based on the behavior of the abundances of the four molecules considered here as a function of this emission. The variations of the first three types could also be attributed to the variation of the H2 column density.

astro-ph.GA↗