arXiv subjects

Juan Deng

Publications and source records attributed to Juan Deng.

8 recordsLinked to original sources

arXiv2026

The extinction distances for over a thousand planetary nebulae with Gaia measurements

Although Gaia has identified the central stars of planetary nebulae (CSPNe) for about 70% of known Galactic planetary nebulae (PNe), reliable distance estimates remain incomplete, with fewer than one quarter having accurate parallaxes. Meanwhile, classical extinction-distance samples include only about 70 objects, corresponding to 1.8% of the Galactic PN population. We aim to construct a large and homogeneous catalogue of PN distances by refining extinction-distance measurements with Gaia DR3, providing an independent complement to CSPN parallax-based distances. We develop a Gaia-based extinction-distance method by combining an improved blue-edge approach with an extinction-jump model. PN distances are derived from stellar extinction jumps in line-of-sight extinction-distance profiles and are further constrained by comparisons with published distances, the spatial distribution of stars relative to the PN centre, and the PN radius-distance relation. We obtain distances for 1,066 PNe with a median relative uncertainty of 13%, with about 87% of the sample having uncertainties below 20%. The catalogue includes 765 PNe whose CSPN parallaxes have uncertainties greater than 20% and 128 PNe without CSPN parallaxes. This method complements CSPN parallax-based approaches and extends the traditional extinction-based method to higher Galactic latitudes. For PNe with discrepant literature distances, it helps identify the more reliable estimates and assess CSPN identifications. We find a likely misidentification of the reported CSPN for Fr2-36 and analyse 33 PNe with two CSPN candidates, suggesting improved identifications for 15 objects. This catalogue represents the largest homogeneous set of extinction-based PN distances to date and provides a robust benchmark for studies of Galactic structure, PN populations, and interstellar extinction.

astro-ph.GA↗

arXiv2026

Full-channel wavefront manipulation of surface waves with chirality-assisted geometric-phase metasurface

Owing to their localized field enhancement and subwavelength resolution, surface waves (SWs) offer broad application potential in communications, sensing, and photonics via on-chip wavefront manipulation. This makes multi-channel SW wavefront manipulation highly desirable. However, conventional metasurfaces for SW wavefront shaping, relying on geometric and propagation phase mechanisms, typically exhibit similar functionalities for co- or cross-polarized output channels under different circularly polarized (CP) incidences, thereby limiting the development of high-capacity on-chip integrated devices. Here, by introducing the chirality-assisted phase as an additional phase control mechanism, we effectively decouple both co- and cross-polarized output channels, enabling independent SW wavefront shaping in four distinct channels. We numerically and experimentally demonstrate two metasurfaces in the microwave range: a four-channel SW meta-deflector and a four-channel SW metadevice that simultaneously produces a focused SW beam, a SW Bessel beam, and two deflected SW beams in different directions. Therefore, chirality-assisted geometric-phase metasurfaces provide a versatile platform for multi-channel SW wavefront engineering, offering significant potential for high-capacity on-chip communication and integrated photonic systems.

physics.optics↗

arXiv2025

STCKGE:Continual Knowledge Graph Embedding Based on Spatial Transformation

Current Continual Knowledge Graph Embedding (CKGE) methods primarily rely on translation-based embedding approaches, leveraging previously acquired knowledge to initialize new facts. While these methods often integrate fine-tuning or continual learning strategies to enhance efficiency, they compromise prediction accuracy and lack support for complex relational structures (e.g., multi-hop relations). To address these limitations, we propose STCKGE, a novel CKGE framework based on spatial transformation. In this framework, entity positions are jointly determined by base position vectors and offset vectors, enabling the model to represent complex relations more effectively while supporting efficient embedding updates for both new and existing knowledge through simple spatial operations, without relying on traditional continual learning techniques. Furthermore, we introduce a bidirectional collaborative update strategy and a balanced embedding method to guide parameter updates, effectively minimizing training costs while improving model accuracy. We comprehensively evaluate our model on seven public datasets and a newly constructed dataset (MULTI) focusing on multi-hop relationships. Experimental results confirm STCKGE's strong performance in multi-hop relationship learning and prediction accuracy, with an average MRR improvement of 5.4\%.

cs.IR↗

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

The Multi-wavelength Extinction Law and its Variation in the Coalsack Molecular Cloud Based on the Gaia, APASS, SMSS, 2MASS, GLIMPSE, and WISE Surveys

Accurate interpretation of observations relies on the interstellar dust extinction law, which also serves as a powerful diagnostic for probing dust properties. In this study, we investigate the multi-wavelength extinction law of the quiescent, starless molecular cloud Coalsack and explore its potential variation across different interstellar environments: the surrounding region, the nearby high Galactic latitude region, the inner dense region, and the inner diffuse region. Using a sample of 368,524 dwarf stars selected from Gaia DR3 as tracers, we establish the effective temperature Teff-intrinsic color relations to derive the intrinsic color indices and optical-mid-infrared (MIR) color excess (CE) for 20 bands. Linear fits to the CE-CE diagrams provide color excess ratios (CERs), which are subsequently converted into relative extinction. The resulting extinction curves for different environments exhibit steep slopes in the near-infrared (NIR) and flat profiles in the MIR. In the optical-NIR range, the Coalsack extinction law is consistent with R_V = 3.1 while in the MIR it follows R_V= 5.5 similar to the results of active star-forming clouds. At an angular resolution of 1.3', our extinction map reveals fine cloud structures. No correlation is found between R_V and E(B-V) for E(B-V) > 0.3 mag, implying a uniform optical extinction law in the Coalsack cloud. The derived average R_V value is 3.24.

astro-ph.GA↗

arXiv2024

A New Method in Facial Registration in Clinics Based on Structure Light Images

Background and Objective: In neurosurgery, fusing clinical images and depth images that can improve the information and details is beneficial to surgery. We found that the registration of face depth images was invalid frequently using existing methods. To abundant traditional image methods with depth information, a method in registering with depth images and traditional clinical images was investigated. Methods: We used the dlib library, a C++ library that could be used in face recognition, and recognized the key points on faces from the structure light camera and CT image. The two key point clouds were registered for coarse registration by the ICP method. Fine registration was finished after coarse registration by the ICP method. Results: RMSE after coarse and fine registration is as low as 0.995913 mm. Compared with traditional methods, it also takes less time. Conclusions: The new method successfully registered the facial depth image from structure light images and CT with a low error, and that would be promising and efficient in clinical application of neurosurgery.

cs.CV↗

arXiv2023

Ten issues of NetGPT

With the rapid development and application of foundation models (FMs), it is foreseeable that FMs will play an important role in future wireless communications. As current Artificial Intelligence (AI) algorithms applied in wireless networks are dedicated models that aim for different neural network architectures and objectives, drawbacks in aspects of generality, performance gain, management, collaboration, etc. need to be conquered. In this paper, we define NetGPT (Network Generative Pre-trained Transformer) -- the foundation models for wireless communications, and summarize ten issues regarding design and application of NetGPT.

cs.NI↗

arXiv2014

Lie ring isomorphisms between nest algebras on Banach spaces

Let ${\mathcal N}$ and ${\mathcal M}$ be nests on Banach spaces $X$ and $Y$ over the (real or complex) field $\mathbb F$ and let $\mbox{\rm Alg}{\mathcal N}$ and $\mbox{\rm Alg}{\mathcal M}$ be the associated nest algebras, respectively. It is shown that a map $Φ:{\rm Alg}{\mathcal N}\rightarrow{\rm Alg}{\mathcal M}$ is a Lie ring isomorphism (i.e., $Φ$ is additive, Lie multiplicative and bijective) if and only if $Φ$ has the form $Φ(A) = TAT^{-1} + h(A)I$ for all $A\in \mbox{\rm Alg}{\mathcal N}$ or $Φ(A)=-TA^*T^{-1}+h(A)I$ for all $A\in \mbox{\rm Alg}{\mathcal N}$, where $h$ is an additive functional vanishing on all commutators and $T$ is an invertible bounded linear or conjugate linear operator when $\dim X=\infty$; $T$ is a bijective $τ$-linear transformation for some field automorphism $τ$ of $\mathbb F$ when $\dim X<\infty$.

math.FA↗