arXiv subjects

Nan Li

Publications and source records attributed to Nan Li.

At least 127 records · Page 7Linked to original sources

arXiv2025

Goal-oriented Semantic Communications for Metaverse Construction via Generative AI and Optimal Transport

The emergence of the metaverse has boosted productivity and creativity, driving real-time updates and personalized content, which will substantially increase data traffic. However, current bit-oriented communication networks struggle to manage this high volume of dynamic information, restricting metaverse applications interactivity. To address this research gap, we propose a goal-oriented semantic communication (GSC) framework for metaverse. Building on an existing metaverse wireless construction task, our proposed GSC framework includes an hourglass network-based (HgNet) encoder to extract semantic information of objects in the metaverse; and a semantic decoder that uses this extracted information to reconstruct the metaverse content after wireless transmission, enabling efficient communication and real-time object behaviour updates to the scenery for metaverse construction task. To overcome the wireless channel noise at the receiver, we design an optimal transport (OT)-enabled semantic denoiser, which enhances the accuracy of metaverse scenery through wireless communication. Experimental results show that compared to the conventional metaverse construction, our proposed GSC framework significantly reduces wireless metaverse construction latency by 92.6\%, while improving metaverse object status accuracy and viewing experience by 45.6\% and 44.7\%, respectively.

eess.SY↗

arXiv2025

The Universe is Odd

The cosmological principle posits that the universe does not exhibit any specific preference for position or direction. However, it remains unclear whether the universe has a distinct preference for parity: whether certain properties are more likely to be classified as even or odd. In this study, we analyze the largest available galaxy group catalogs to explore this hypothesis: specifically, whether the number of galaxies within a galaxy group or cluster is more likely to be odd or even. Our findings convincingly indicate that the universe indeed favors odd numbers, with results achieving a significance level well above the $4.1-σ$ threshold.

physics.pop-ph↗

arXiv2025

Data-driven $H_{\infty}$ predictive control for constrained systems: a Lagrange duality approach

This article proposes a data-driven $H_{\infty}$ control scheme for time-domain constrained systems based on model predictive control formulation. The scheme combines $H_{\infty}$ control and minimax model predictive control, enabling more effective handling of external disturbances and time-domain constraints. First, by leveraging input-output-disturbance data, the scheme ensures $H_{\infty}$ performance of the closed-loop system. Then, a minimax optimization problem is converted into a more manageable minimization problem employing Lagrange duality, which reduces conservatism typically associated with ellipsoidal evaluations of time-domain constraints. The study examines key closed-loop properties, including stability, disturbance attenuation, and constraint satisfaction, achieved by the proposed data-driven moving horizon predictive control algorithm. The effectiveness and advantages of the proposed method are demonstrated through numerical simulations involving a batch reactor system, confirming its robustness and feasibility under noisy conditions.

math.OC↗

arXiv2025

Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks.

cs.NI↗

arXiv2025

PINN-MG: A physics-informed neural network for mesh generation

In numerical simulation, structured mesh generation often requires a lot of time and manpower investment. The general scheme for structured quad mesh generation is to find a mapping between the computational domain and the physical domain. This mapping can be obtained by solving partial differential equations. However, existing structured mesh generation methods are difficult to ensure both efficiency and mesh quality. In this paper, we propose a structured mesh generation method based on physics-informed neural network, PINN-MG. It takes boundary curves as input and then utilizes an attention network to capture the potential mapping between computational and physical domains, generating structured meshes for the input physical domain. PINN-MG introduces the Navier-Lamé equation in linear elastic as a partial differential equation term in the loss function, ensuring that the neural network conforms to the law of elastic body deformation when optimizing the loss value. The training process of PINN-MG is completely unsupervised and does not require any prior knowledge or datasets, which greatly reduces the previous workload of producing structured mesh datasets. Experimental results show that PINN-MG can generate higher quality structured quad meshes than other methods, and has the advantages of traditional algebraic methods and differential methods.

cs.CE↗

arXiv2025

Goal-Oriented Semantic Communication for Wireless Image Transmission via Stable Diffusion

Efficient image transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality image reconstruction over a bandwidth-constrained noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented semantic communication (GSC) framework. In this framework, we design a semantic autoencoder that effectively extracts semantic information (SI) from images to reduce the transmission data size while ensuring high-quality reconstruction. Recognizing the impact of wireless channel noise on SI transmission, we propose an SD-based denoiser for GSC (SD-GSC) conditional on an instantaneous channel gain to remove the channel noise from the received noisy SI under known channel. For scenarios with unknown channel, we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn the distribution of channel gains and denoise the received SI. It is shown that, with the known channel, our SD-GSC outperforms state-of-the-art ADJSCC and Latent-Diff DNSC, improving Peak Signal-to-Noise Ratio (PSNR) by 32% and 21%, and reducing Fréchet Inception Distance (FID) by 40% and 35%, respectively. With the unknown channel, our PSD-GSC improves PSNR by 8% and reduces FID by 17% compared to MMSE equalizer-enhanced SD-GSC.

eess.IV↗

arXiv2025

Goal-Oriented Semantic Communication for Wireless Video Transmission via Generative AI

Efficient video transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality video transmission over a bandwidth-constrained noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented semantic communication (GSC) framework. In this framework, we first design a semantic encoder that effectively identify the keyframes from video and extract the relevant semantic information (SI) to reduce the transmission data size. We then develop a semantic decoder to reconstruct the keyframes from the received SI and further generate the full video from the reconstructed keyframes using frame interpolation to ensure high-quality reconstruction. Recognizing the impact of wireless channel noise on SI transmission, we also propose an SD-based denoiser for GSC (SD-GSC) condition on an instantaneous channel gain to remove the channel noise from the received noisy SI under a known channel. For scenarios with an unknown channel, we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn the distribution of channel gains and denoise the received SI. It is shown that, with the known channel, our proposed SD-GSC outperforms state-of-the-art ADJSCC, Latent-Diff DNSC, DeepWiVe and DVST, improving Peak Signal-to-Noise Ratio (PSNR) by 69%, 58%, 33% and 38%, reducing mean squared error (MSE) by 52%, 50%, 41% and 45%, and reducing Fréchet Video Distance (FVD) by 38%, 32%, 22% and 24%, respectively. With the unknown channel, our PSD-GSC achieves a 17% improvement in PSNR, a 29% reduction in MSE, and a 19% reduction in FVD compared to MMSE equalizer-enhanced SD-GSC. These significant performance improvements demonstrate the robustness and superiority of our proposed methods in enhancing video transmission quality and efficiency under various channel conditions.

eess.IV↗

arXiv2025

The Evolution of Size and Merger Fraction of Submillimeter Galaxies across $1 < z \lesssim 6$ as Observed by JWST

Precise tracking of the growth in galaxy size and the evolution of merger fractions with redshift is vital for understanding the formation history of submillimeter galaxies (SMGs). This study investigates these evolutions over a broad redshift range ($1 < z \lesssim 6$), using a sample of 222 SMGs with a median redshift of $z = 2.61^{+0.89}_{-0.82}$ identified by ALMA and JCMT, enhanced by the advanced imaging capabilities of the JWST/NIRCam and MIRI. We find significant evolution in effective radii ($R_e$) in rest-frame V-band ($R_e \propto (1 + z)^{-0.87 \pm 0.08}$) and near-infrared (NIR) band ($R_e \propto (1 + z)^{-0.88 \pm 0.11}$), with the NIR size evolution resembling that of massive star-forming galaxies at lower redshift. Visual inspections reveal a major merger fraction of $24.3 \pm 3.7\%$ and an interaction fraction of up to $48.4 \pm 11.1\%$. The major merger fraction exhibits an increase from 14.7$\pm9.1$\% at $z = 1$ to 26.6$\pm 8.4$\% at $z = 3$, after which it remains approximately constant across the redshift range $3 < z < 6$. In contrast, the interaction fraction remains relatively stable across the range $2 < z < 5$. Our results indicate that late-stage major mergers are not the primary formation mechanism for SMGs at $z<3$, while interactions appear to play a significant role across the broader redshift range of $1<z<6$. Additionally, HST-based major merger identifications may overestimate the true fraction by a factor of 1.7 at $z \sim 2$. These findings highlight the varying roles of mergers and interactions in driving the formation of massive, dusty star-forming galaxies across different redshifts.

astro-ph.GA↗

arXiv2025

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.

q-bio.OT↗

arXiv2025

Primordial black holes from the ultraslow-roll phase in the inflaton--curvaton mixed field inflation

Primordial black holes (PBHs) are a promising candidate for dark matter, as they can form in the very early universe without invoking new particle physics. This work explores PBH formation within a curvaton scenario featuring an ultraslow-roll (USR) phase. An inflaton--curvaton mixed field model is presented, where the inflaton drives early inflation and then transits into the USR phase, amplifying the small-scale curvature perturbation. During inflation, the curvaton generates entropy perturbation, which later converts into curvature perturbation after the curvaton decays in the radiation-dominated era. Using the $δN$ formalism, we compute the power spectrum of the total primordial curvature perturbation and analyze the relevant non-Gaussianity. Our results show that adding a curvaton field not only has a significant impact on primordial non-Gaussianity, but also introduces more complex inflationary dynamics, even saving the inflaton potentials that generate too low scalar spectral indices. Our model can produce PBHs with mass around $10^{-14}\,M_\odot$ that account for all dark matter, while remaining consistent with current observational constraints.

astro-ph.CO↗

arXiv2025

Data-Enabled Predictive Control for Flexible Spacecraft

Spacecraft are vital to space exploration and are often equipped with lightweight, flexible appendages to meet strict weight constraints. These appendages pose significant challenges for modeling and control due to their inherent nonlinearity. Data-driven control methods have gained traction to address such challenges. This paper introduces, to the best of the authors' knowledge, the first application of the data-enabled predictive control (DeePC) framework to boundary control for flexible spacecraft. Leveraging the fundamental lemma, DeePC constructs a non-parametric model by utilizing recorded past trajectories, eliminating the need for explicit model development. The developed method also incorporates dimension reduction techniques to enhance computational efficiency. Through comprehensive numerical simulations, this study compares the proposed method with Lyapunov-based control, demonstrating superior performance and offering a thorough evaluation of data-driven control for flexible spacecraft.

eess.SY↗

arXiv2025

WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication

This paper introduces WirelessGPT, a pioneering foundation model specifically designed for multi-task learning in wireless communication and sensing. Specifically, WirelessGPT leverages large-scale wireless channel datasets for unsupervised pretraining and extracting universal channel representations, which captures complex spatiotemporal dependencies. In fact,this task-agnostic design adapts WirelessGPT seamlessly to a wide range of downstream tasks, using a unified representation with minimal fine-tuning. By unifying communication and sensing functionalities, WirelessGPT addresses the limitations of task-specific models, offering a scalable and efficient solution for integrated sensing and communication (ISAC). With an initial parameter size of around 80 million, WirelessGPT demonstrates significant improvements over conventional methods and smaller AI models, reducing reliance on large-scale labeled data. As the first foundation model capable of supporting diverse tasks across different domains, WirelessGPT establishes a new benchmark, paving the way for future advancements in multi-task wireless systems.

cs.LG↗

arXiv2025

Flexible delivery of high-power picosecond laser in purely-single optical mode of anti-resonant hollow-core fiber for micromachining

We present the flexible delivery of picosecond laser pulses with up to 20 W average power over a 3-m-long sample of anti-resonant hollow-core fiber (AR-HCF) for laser micromachining applications. Our experiments highlight the importance of optical mode purity of the AR-HCF for the manufacturing precision. We demonstrate that compared with an AR-HCF sample with a capillary to core (d/D) ratio of ~0.5, the AR-HCF with a d/D ratio of ~0.68 exhibits better capability of high-order-mode suppression, giving rise to improved micromachining quality. Moreover, the AR-HCF delivery system exhibits better pointing stability and set-up flexibility than the free-space beam delivery system. These results pave the way to practical applications of AR-HCF in developing advanced equipment for ultrafast laser micromachining.

physics.optics↗

arXiv2025

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

Federated Learning (FL) enables clients to collaboratively train machine learning models without sharing local data, preserving privacy in diverse environments. While traditional FL approaches preserve privacy, they often struggle with high computational and communication overhead. To address these issues, model pruning is introduced as a strategy to streamline computations. However, existing pruning methods, when applied solely based on local data, often produce sub-models that inadequately reflect clients' specific tasks due to data insufficiency. To overcome these challenges, this paper introduces SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalized federated learning through client-specific clustering and Similar Client Structure Information (SCSI)-guided model pruning. SAFL employs a two-stage process: initially, it groups clients based on data similarities and uses aggregated pruning criteria to guide the pruning process, facilitating the identification of optimal sub-models. Subsequently, clients train these pruned models and engage in server-based aggregation, ensuring tailored and efficient models for each client. This method significantly reduces computational overhead while improving inference accuracy. Extensive experiments demonstrate that SAFL markedly diminishes model size and improves performance, making it highly effective in federated environments characterized by heterogeneous data.

cs.LG↗

arXiv2025

Occamy: A Preemptive Buffer Management for On-chip Shared-memory Switches

Today's high-speed switches employ an on-chip shared packet buffer. The buffer is becoming increasingly insufficient as it cannot scale with the growing switching capacity. Nonetheless, the buffer needs to face highly intense bursts and meet stringent performance requirements for datacenter applications. This imposes rigorous demand on the Buffer Management (BM) scheme, which dynamically allocates the buffer across queues. However, the de facto BM scheme, designed over two decades ago, is ill-suited to meet the requirements of today's network. In this paper, we argue that shallow-buffer switches, intense bursts, along with dynamic traffic call for a highly agile BM that can quickly adjust the buffer allocation as traffic changes. However, the agility of the current BM is fundamentally limited by its non-preemptive nature. Nonetheless, we find that preemptive BM, considered unrealizable in history, is now feasible on modern switch chips. We propose Occamy, a preemptive BM that can quickly adjust buffer allocation. Occamy utilizes the redundant memory bandwidth to actively reclaim and reallocate the over-allocated buffer. Testbed experiments and large-scale simulations show that Occamy can improve the end-to-end performance by up to ~55%.

cs.NI↗

arXiv2025

Discovery of 19 strongly-lensed quasars, dual and projected quasars in DESI-LS

We report the follow-up spectroscopic confirmation of two lensed quasars, six dual quasars, and eleven projected quasars that were previously identified as lensed-quasar candidates in \cite{He2023}. The spectroscopic data were obtained from two different sources: the P200/DBSP in California and publicly available datasets, including SDSS and DESI-EDR. The two lensed quasars (both pairs) have the following properties: $θ_E$ = 1.208'', $z_s$ = 3.105; $θ_E$ = 0.749, $z_s$ = 2.395. The six dual quasars have redshifts ranging from 0.58 to 3.28 and projected separations ranging from 15.44 to 22.54 kpc, with a mean separation of 17.95 kpc. The eleven projected quasars have projected separations ranging from 10.96 to 39.07 kpc, with a mean separation of 22.64 kpc. Additionally, there are three likely lensed quasars that cannot be definitively confirmed, contributed by two reasons. Firstly, their image separations (0.83'', 0.98'', and 0.93'') are small compared to the seeing conditions during our observations (around 1.2''). Secondly, no high SNR lensing galaxy can be detected in the Legacy Survey Imaging. Better spectroscopy and (or) imaging are needed to confirm their lensing nature.

astro-ph.GA↗

arXiv2025

Environmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment Domain

Generative AI holds significant potential for ecological and environmental applications such as monitoring, data analysis, education, and policy support. However, its effectiveness is limited by the lack of a unified evaluation framework. To address this, we present the Environmental Large Language model Evaluation (ELLE) question answer (QA) dataset, the first benchmark designed to assess large language models and their applications in ecological and environmental sciences. The ELLE dataset includes 1,130 question answer pairs across 16 environmental topics, categorized by domain, difficulty, and type. This comprehensive dataset standardizes performance assessments in these fields, enabling consistent and objective comparisons of generative AI performance. By providing a dedicated evaluation tool, ELLE dataset promotes the development and application of generative AI technologies for sustainable environmental outcomes. The dataset and code are available at https://elle.ceeai.net/ and https://github.com/CEEAI/elle.

cs.CL↗

arXiv2025

Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

Remote sensing object detection is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our model achieves state-of-the-art performance for few-shot object detection in remote sensing.

cs.CV↗