arXiv subjects

Liang Li

Publications and source records attributed to Liang Li.

At least 127 records · Page 7Linked to original sources

arXiv2025

Multimessenger signatures of a deformed magnetar in gamma-ray bursts

We study the evolution of a newly formed magnetized neutron-star (NS) as a power source of gamma-ray bursts (GRBs) in the light of both gravitational-wave (GW) and electromagnetic (EM) radiations. The compressible and incompressible fluids are employed in order to model the secular evolution of stable Maclaurian spheroids. It is shown that the GW and EM emissions evolve as a function of eccentricity and rotational frequency with time. We find that the luminosity characteristics crucially depend on NS parameters such as magnitude and structure of magnetic field, ellipticity and the equation of state (EoS) of the fluid. The presence of X-ray flares, whose origins are not yet well understood, can be captured in our model regarding some specific nuclear EoSs. Our model allowing us to explain flares that occur within the wide range of $ 10$ to $10^4$ s and the peak EM luminosity in the order of $10^{46}$ - $10^{51}$ $\rm \text{erg} s^{-1}$ by using a reasonable set of parameters, such as magnetic field strength around $10^{14}-10^{16}$ G, the quadrupole-to-dipole ratio of magnetic field up to 500. By applying our model to a sample of GRB X-ray flares observed by the Swift/X-ray Telescope, we try to constraint the crucial parameters of a deformed magnetar via a Marcov Chain Monte Carlo fitting method. Our analysis shows that ongoing and upcoming joint multimessenger detections can be used to understand the nature of a GRB's central engine and its evolution at the early times of the burst formation.

astro-ph.HE↗

arXiv2025

Seedream 3.0 Technical Report

We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in Seedream 2.0, including alignment with complicated prompts, fine-grained typography generation, suboptimal visual aesthetics and fidelity, and limited image resolutions. Specifically, the advancements of Seedream 3.0 stem from improvements across the entire pipeline, from data construction to model deployment. At the data stratum, we double the dataset using a defect-aware training paradigm and a dual-axis collaborative data-sampling framework. Furthermore, we adopt several effective techniques such as mixed-resolution training, cross-modality RoPE, representation alignment loss, and resolution-aware timestep sampling in the pre-training phase. During the post-training stage, we utilize diversified aesthetic captions in SFT, and a VLM-based reward model with scaling, thereby achieving outputs that well align with human preferences. Furthermore, Seedream 3.0 pioneers a novel acceleration paradigm. By employing consistent noise expectation and importance-aware timestep sampling, we achieve a 4 to 8 times speedup while maintaining image quality. Seedream 3.0 demonstrates significant improvements over Seedream 2.0: it enhances overall capabilities, in particular for text-rendering in complicated Chinese characters which is important to professional typography generation. In addition, it provides native high-resolution output (up to 2K), allowing it to generate images with high visual quality.

cs.CV↗

arXiv2025

Seedance 1.0: Exploring the Boundaries of Video Generation Models

Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously balancing prompt following, motion plausibility, and visual quality. In this report, we introduce Seedance 1.0, a high-performance and inference-efficient video foundation generation model that integrates several core technical improvements: (i) multi-source data curation augmented with precision and meaningful video captioning, enabling comprehensive learning across diverse scenarios; (ii) an efficient architecture design with proposed training paradigm, which allows for natively supporting multi-shot generation and jointly learning of both text-to-video and image-to-video tasks. (iii) carefully-optimized post-training approaches leveraging fine-grained supervised fine-tuning, and video-specific RLHF with multi-dimensional reward mechanisms for comprehensive performance improvements; (iv) excellent model acceleration achieving ~10x inference speedup through multi-stage distillation strategies and system-level optimizations. Seedance 1.0 can generate a 5-second video at 1080p resolution only with 41.4 seconds (NVIDIA-L20). Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation.

cs.CV↗

arXiv2025

LLM-Empowered IoT for 6G Networks: Architecture, Challenges, and Solutions

The Internet of Things (IoT) in the sixth generation (6G) era is envisioned to evolve towards intelligence, ubiquity, and self-optimization. Large language models (LLMs) have demonstrated remarkable generalization capabilities across diverse domains, including natural language processing (NLP), computer vision (CV), and beyond. In this article, we propose an LLM-empowered IoT architecture for 6G networks to achieve intelligent autonomy while supporting advanced IoT applications. LLMs are pushed to the edge of the 6G network to support the synergy of LLMs and IoT. LLM solutions are tailored to both IoT application requirements and IoT management needs, i.e., LLM for IoT. On the other hand, edge inference and edge fine-tuning are discussed to support the deployment of LLMs, i.e., LLM on IoT. Furthermore, we propose a memory-efficient split federated learning (SFL) framework for LLM fine-tuning on heterogeneous IoT devices that alleviates memory pressures on both IoT devices and the edge server while achieving comparable performance and convergence time. Finally, a case study is presented, followed by a discussion about open issues of LLM-empowered IoT for 6G networks.

cs.ET↗

arXiv2025

MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation

With the rapid adoption of large language models (LLMs) in natural language processing, the ability to follow instructions has emerged as a key metric for evaluating their practical utility. However, existing evaluation methods often focus on single-language scenarios, overlooking the challenges and differences present in multilingual and cross-lingual contexts. To address this gap, we introduce MaXIFE: a comprehensive evaluation benchmark designed to assess instruction-following capabilities across 23 different languages with 1667 verifiable instruction tasks. MaXIFE integrates both Rule-Based Evaluation and Model-Based Evaluation, ensuring a balance of efficiency and accuracy. We applied MaXIFE to evaluate several leading commercial LLMs, establishing baseline results for future comparisons. By providing a standardized tool for multilingual instruction-following evaluation, MaXIFE aims to advance research and development in natural language processing.

cs.CL↗

arXiv2025

A TRPCA-Inspired Deep Unfolding Network for Hyperspectral Image Denoising via Thresholded t-SVD and Top-K Sparse Transformer

Hyperspectral images (HSIs) are often degraded by complex mixed noise during acquisition and transmission, making effective denoising essential for subsequent analysis. Recent hybrid approaches that bridge model-driven and data-driven paradigms have shown great promise. However, most of these approaches lack effective alternation between different priors or modules, resulting in loosely coupled regularization and insufficient exploitation of their complementary strengths. Inspired by tensor robust principal component analysis (TRPCA), we propose a novel deep unfolding network (DU-TRPCA) that enforces stage-wise alternation between two tightly integrated modules: low-rank and sparse. The low-rank module employs thresholded tensor singular value decomposition (t-SVD), providing a widely adopted convex surrogate for tensor low-rankness and has been demonstrated to effectively capture the global spatial-spectral structure of HSIs. The Top-K sparse transformer module adaptively imposes sparse constraints, directly matching the sparse regularization in TRPCA and enabling effective removal of localized outliers and complex noise. This tightly coupled architecture preserves the stage-wise alternation between low-rank approximation and sparse refinement inherent in TRPCA, while enhancing representational capacity through attention mechanisms. Extensive experiments on synthetic and real-world HSIs demonstrate that DU-TRPCA surpasses state-of-the-art methods under severe mixed noise, while offering interpretability benefits and stable denoising dynamics inspired by iterative optimization. Code is available at https://github.com/liangli97/TRPCA-Deep-Unfolding-HSI-Denoising.

cs.CV↗

arXiv2025

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices

In this paper, we propose an edge-assisted split federated learning framework to facilitate large language model (LLM) fine-tuning on heterogeneous mobile devices while alleviating memory pressures on both mobile devices and the edge server. Specifically, mobile devices perform low-rank adaptation (LoRA) fine-tuning on only a subset of lower layers of the pre-trained LLM, tailored to their individual capacities. On the server, a full LLM is maintained, and the corresponding LoRA modules are selectively fine-tuned in a sequential manner for each device. To further enhance training efficiency, we propose a server-side training scheduling method that optimizes the processing order of devices for accelerating fine-tuning. Extensive experiments demonstrate that compared to the baselines, our scheme can reduce 79\% memory footprint and 6\% training time while achieving comparable performance.

cs.DC↗

arXiv2025

Observation of universal topological magnetoelectric switching in multiferroic GdMn2O5

Topological magnetoelectricity was recently revealed as an emergent topic, which opens a unique route to precisely control magnetoelectric functionality. Here we report the synchronous magnetic-electric-cycle operation of topological magnetoelectric switching in GdMn2O5. Compared with pure magnetic-cycle operation, this topological winding can be accessed in a much broader parameter space, i.e. orientation of magnetic field is not limited to the magic angle and the effect can persist up to the Curie temperature. The fine tuning of free energy landscape is responsible to this topological behavior.

cond-mat.mtrl-sci↗

arXiv2025

SafeCFG: Controlling Harmful Features with Dynamic Safe Guidance for Safe Generation

Diffusion models (DMs) have demonstrated exceptional performance in text-to-image tasks, leading to their widespread use. With the introduction of classifier-free guidance (CFG), the quality of images generated by DMs is significantly improved. However, one can use DMs to generate more harmful images by maliciously guiding the image generation process through CFG. Existing safe alignment methods aim to mitigate the risk of generating harmful images but often reduce the quality of clean image generation. To address this issue, we propose SafeCFG to adaptively control harmful features with dynamic safe guidance by modulating the CFG generation process. It dynamically guides the CFG generation process based on the harmfulness of the prompts, inducing significant deviations only in harmful CFG generations, achieving high quality and safety generation. SafeCFG can simultaneously modulate different harmful CFG generation processes, so it could eliminate harmful elements while preserving high-quality generation. Additionally, SafeCFG provides the ability to detect image harmfulness, allowing unsupervised safe alignment on DMs without pre-defined clean or harmful labels. Experimental results show that images generated by SafeCFG achieve both high quality and safety, and safe DMs trained in our unsupervised manner also exhibit good safety performance.

cs.CV↗

arXiv2025

Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact

Modeling complex semiconductor fabrication processes such as Ohmic contact formation remains challenging due to high-dimensional parameter spaces and limited experimental data. While classical machine learning (CML) approaches have been successful in many domains, their performance degrades in small-sample, nonlinear scenarios. In this work, we investigate quantum machine learning (QML) as an alternative, exploiting quantum kernels to capture intricate correlations from compact datasets. Using only 159 experimental GaN HEMT samples, we develop a quantum kernel-aligned regressor (QKAR) combining a shallow Pauli-Z feature map with a trainable quantum kernel alignment (QKA) layer. All models, including seven baseline CML regressors, are evaluated under a unified PCA-based preprocessing pipeline to ensure a fair comparison. QKAR consistently outperforms classical baselines across multiple metrics (MAE, MSE, RMSE), achieving a mean absolute error of 0.338 Omega mm when validated on experimental data. We further assess noise robustness and generalization through cross-validation and new device fabrication. These findings suggest that carefully constructed QML models could provide predictive advantages in data-constrained semiconductor modeling, offering a foundation for practical deployment on near-term quantum hardware. While challenges remain for both QML and CML, this study demonstrates QML's potential as a complementary approach in complex process modeling tasks.

cs.LG↗

arXiv2025

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation

This paper explores the Class-Incremental Source-Free Unsupervised Domain Adaptation (CI-SFUDA) problem, where the unlabeled target data come incrementally without access to labeled source instances. This problem poses two challenges, the interference of similar source-class knowledge in target-class representation learning and the shocks of new target knowledge to old ones. To address them, we propose the Multi-Granularity Class Prototype Topology Distillation (GROTO) algorithm, which effectively transfers the source knowledge to the class-incremental target domain. Concretely, we design the multi-granularity class prototype self-organization module and the prototype topology distillation module. First, we mine the positive classes by modeling accumulation distributions. Next, we introduce multi-granularity class prototypes to generate reliable pseudo-labels, and exploit them to promote the positive-class target feature self-organization. Second, the positive-class prototypes are leveraged to construct the topological structures of source and target feature spaces. Then, we perform the topology distillation to continually mitigate the shocks of new target knowledge to old ones. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on three public datasets. Code is available at https://github.com/dengpeihua/GROTO.

cs.CV↗

arXiv2025

Multi-Layer Backward Joint Model for Dynamic Prediction of Clinical Events with Multivariate Longitudinal Predictors of Mixed Types

Dynamic prediction of time-to-event outcomes using longitudinal data is highly useful in clinical research and practice. A common strategy is the joint modeling of longitudinal and time-to-event data. The shared random effect model has been widely studied for this purpose. However, it can be computationally challenging when applied to problems with a large number of longitudinal predictor variables, particularly when mixed types of continuous and categorical variables are involved. Addressing these limitations, we introduce a novel multi-layer backward joint model (MBJM). The model structure consists of multiple data layers cohesively integrated through a series of conditional distributions that involve longitudinal and time-to-event data, where the time to the clinical event is the conditioning variable. This model can be estimated with standard statistical software with rapid and robust computation, regardless of the dimension of the longitudinal predictor variables. We provide both theoretical and empirical results to show that the MBJM outperforms the static prediction model that does not fully account for the longitudinal nature of the prediction. In an empirical comparison with the shared random effects joint model, the MBJM demonstrated competitive performance with substantially faster and more robust computation. Both the simulation and real data application from a primary biliary cirrhosis study utilized seven longitudinal biomarkers, five continuous and two categorical, larger than the typically published joint modeling problems.

stat.ME↗

arXiv2025

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment

This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2025. The aim of this challenge is to address the fine-grained quality assessment of text-to-image generation models. This challenge evaluates text-to-image models from two aspects: image-text alignment and image structural distortion detection, and is divided into the alignment track and the structural track. The alignment track uses the EvalMuse-40K, which contains around 40K AI-Generated Images (AIGIs) generated by 20 popular generative models. The alignment track has a total of 371 registered participants. A total of 1,883 submissions are received in the development phase, and 507 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. The structure track uses the EvalMuse-Structure, which contains 10,000 AI-Generated Images (AIGIs) with corresponding structural distortion mask. A total of 211 participants have registered in the structure track. A total of 1155 submissions are received in the development phase, and 487 submissions are received in the test phase. Finally, 8 participating teams submitted their models and fact sheets. Almost all methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on T2I model quality assessment.

cs.CV↗

arXiv2025

FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection

Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can remarkably reduce its training latency over homogeneous clients, yet encounter severe model staleness, model drifts, memory cost and straggler issues in heterogeneous environments. To unleash the full potential of overlapping, we propose, FedEx, a novel \underline{fed}erated learning approach to \underline{ex}pedite FL training over mobile devices under data, computing and wireless heterogeneity. FedEx redefines the overlapping procedure with staleness ceilings to constrain memory consumption and make overlapping compatible with participation selection (PS) designs. Then, FedEx characterizes the PS utility function by considering the latency reduced by overlapping, and provides a holistic PS solution to address the straggler issue. FedEx also introduces a simple but effective metric to trigger overlapping, in order to avoid model drifts. Experimental results show that compared with its peer designs, FedEx demonstrates substantial reductions in FL training latency over heterogeneous mobile devices with limited memory cost.

cs.DC↗

arXiv2025

Scheduling of Flexible Manufacturing Systems Based on Place-Timed Petri Nets and Basis Reachability Graphs

Scheduling is a key decision-making process to improve the performance of flexible manufacturing systems. Place-timed Petri nets provide a formal method for graphically modeling and analyzing such systems. By generating reachability graphs and combining intelligent search algorithms, operation sequences from the initial state to the target state can be found for the underlying system. However, the reachability graph grows exponentially with the system size increases, which is the main challenge of existing methods for scheduling large systems. To this end, we develop an efficient improved beam search algorithm to optimize the makespan based on a compact representation of reachability graph called basis reachability graph. The key idea behind the proposed method is to form a state together with the basis markings and its corresponding transition sequences, and evaluate the cost of the state based on the resource idle time. Experimental results are conducted on several benchmark systems which show that the developed method improves the search efficiency while ensuring the quality of the solution compared with existing methods.

eess.SY↗

arXiv2025

TexPro: Text-guided PBR Texturing with Procedural Material Modeling

In this paper, we present TexPro, a novel method for high-fidelity material generation for input 3D meshes given text prompts. Unlike existing text-conditioned texture generation methods that typically generate RGB textures with baked lighting, TexPro is able to produce diverse texture maps via procedural material modeling, which enables physically-based rendering, relighting, and additional benefits inherent to procedural materials. Specifically, we first generate multi-view reference images given the input textual prompt by employing the latest text-to-image model. We then derive texture maps through rendering-based optimization with recent differentiable procedural materials. To this end, we design several techniques to handle the misalignment between the generated multi-view images and 3D meshes, and introduce a novel material agent that enhances material classification and matching by exploring both part-level understanding and object-aware material reasoning. Experiments demonstrate the superiority of the proposed method over existing SOTAs, and its capability of relighting.

cs.GR↗

arXiv2025

Harnessing self-sensitized scintillation by supramolecular engineering of CsPbBr3 nanocrystals in dense mesoporous template nanospheres

Perovskite-based nanoscintillators, such as CsPbBr3 nanocrystals (NCs), are emerging as promising candidates for ionizing radiation detection, thanks to their high emission efficiency, rapid response, and facile synthesis. However, their nanoscale dimensions - smaller than the mean free path of secondary carriers - and relatively low emitter density per unit volume, limited by their high molecular weight and reabsorption losses, restrict efficient secondary carrier conversion and hamper their practical deployment. In this work, we introduce a strategy to enhance scintillation performance by organizing NCs into densely packed domains within porous SiO2 mesospheres (MSNs). This engineered architecture achieves up to a 40-fold increase in radioluminescence intensity compared to colloidal NCs, driven by improved retention and conversion of secondary charges, as corroborated by electron release measurements. This approach offers a promising pathway toward developing next-generation nanoscintillators with enhanced performance, with potential applications in high-energy physics, medical imaging, and space technologies.

cond-mat.mtrl-sci↗

arXiv2025

Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation

Recent progress in unified models for image understanding and generation has been impressive, yet most approaches remain limited to single-modal generation conditioned on multiple modalities. In this paper, we present Mogao, a unified framework that advances this paradigm by enabling interleaved multi-modal generation through a causal approach. Mogao integrates a set of key technical improvements in architecture design, including a deep-fusion design, dual vision encoders, interleaved rotary position embeddings, and multi-modal classifier-free guidance, which allow it to harness the strengths of both autoregressive models for text generation and diffusion models for high-quality image synthesis. These practical improvements also make Mogao particularly effective to process interleaved sequences of text and images arbitrarily. To further unlock the potential of unified models, we introduce an efficient training strategy on a large-scale, in-house dataset specifically curated for joint text and image generation. Extensive experiments show that Mogao not only achieves state-of-the-art performance in multi-modal understanding and text-to-image generation, but also excels in producing high-quality, coherent interleaved outputs. Its emergent capabilities in zero-shot image editing and compositional generation highlight Mogao as a practical omni-modal foundation model, paving the way for future development and scaling the unified multi-modal systems.

cs.CV↗