arXiv subjects

Lili Yang

Publications and source records attributed to Lili Yang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Catching TeV emission from GRB 221009A and alike with LHAASO, LACT and SWGO

Gamma-Ray Bursts (GRBs) are the most energetic electromagnetic explosions in the universe. Recently, the Large High Altitude Air Shower Observatory (LHAASO) reported the breakthrough observation of GRB 221009A with gamma-ray energies beyond 13 TeV. This discovery, together with the previous GRB detection well above 100 GeV, confirms the production of very-high-energy (VHE, $\gtrsim 100$ GeV) radiation which might be a common component of all bright GRBs. It is reasonable to expect that bright GRBs are important targets for ground-based gamma-ray experiments. In this work, we estimate the detection rate for current and upcoming ground-based gamma-ray observatories including LHAASO, Large Array of Imaging Atmospheric Cherenkov Telescopes (LACT) and the Southern Wide-field Gamma-ray Observatory (SWGO) under two emission models with GRB~221009A as the template: first, that they all share the same intrinsic VHE spectral shape; second, they have the same environmental parameter and electron spectral index, governing their synchrotron self-Compton (SSC) emission. Using the long GRB luminosity and redshift distribution function obtained from the Fermi-GBM GRB samples, and accounting for the cosmological effects and extra-galactic background light (EBL) absorption, we derive the expected VHE flux at Earth. The sensitivity analysis for LHAASO, the upcoming LACT, and SWGO to evaluate their detection potential across specific redshift and luminosity ranges has been performed. The corresponding 5$σ$ detection rates of 221009A-like GRBs for the two emission models are: LHAASO, 0.04-0.05 yr$^{-1}$; LACT, 0.03-0.06 yr$^{-1}$; SWGO, 0.2-0.4 yr$^{-1}$. These rates can vary by up to $\approx 24\%$ due to different EBL models.

astro-ph.HE↗

arXiv2026

Radio Study of Vela X Cocoon

The evolution of pulsar Wind Nebulae (PWNe) influences how high energy particles in the vicinity are generated and transport. The Vela PWN (only $\sim300$\,pc away), provides a rather rare case between young and well-evolved systems. We therefore performed new 6 and 16\,cm high-resolution observations of the Vela X Cocoon region with the Australia Telescope Compact Array (ATCA). The observations reveal a complex region with a $\sim0.5^\circ$ major curved filament extending to far south from the pulsar, as well as other intersecting filaments and wisps. Our spectral analysis hints its connection with the PWN. Our results also found strongly linearly polarized emission, ordered and tangential $B$-field to the filaments. We find the rotation measure (RM) and polarization fraction (PF) along the filament are anti-correlated with the total intensity. We develop a simple 3D model of a spiral filament to explain these, while the PF distribution requires external interpretations such as interaction with the reverse shock. Comparison with archival data suggests that large scale features like the major filament are generally stable and large motions near the X-ray filament, all these confirm the distinction between radio and X-ray features.

astro-ph.HE↗

arXiv2025

An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice

Rice is one of the most widely cultivated crops globally and has been developed into numerous varieties. The quality of rice during cultivation is primarily determined by its cultivar and characteristics. Traditionally, rice classification and quality assessment rely on manual visual inspection, a process that is both time-consuming and prone to errors. However, with advancements in machine vision technology, automating rice classification and quality evaluation based on its cultivar and characteristics has become increasingly feasible, enhancing both accuracy and efficiency. This study proposes a real-time evaluation mechanism for comprehensive rice grain assessment, integrating a one-stage object detection approach, a deep convolutional neural network, and traditional machine learning techniques. The proposed framework enables rice variety identification, grain completeness grading, and grain chalkiness evaluation. The rice grain dataset used in this study comprises approximately 20,000 images from six widely cultivated rice varieties in China. Experimental results demonstrate that the proposed mechanism achieves a mean average precision (mAP) of 99.14% in the object detection task and an accuracy of 97.89% in the classification task. Furthermore, the framework attains an average accuracy of 97.56% in grain completeness grading within the same rice variety, contributing to an effective quality evaluation system.

cs.CV↗

arXiv2025

An Improved Pure Fully Connected Neural Network for Rice Grain Classification

Rice is a staple food for a significant portion of the world's population, providing essential nutrients and serving as a versatile in-gredient in a wide range of culinary traditions. Recently, the use of deep learning has enabled automated classification of rice, im-proving accuracy and efficiency. However, classical models based on first-stage training may face difficulties in distinguishing between rice varieties with similar external characteristics, thus leading to misclassifications. Considering the transparency and feasibility of model, we selected and gradually improved pure fully connected neural network to achieve classification of rice grain. The dataset we used contains both global and domestic rice images obtained from websites and laboratories respectively. First, the training mode was changed from one-stage training to two-stage training, which significantly contributes to distinguishing two similar types of rice. Secondly, the preprocessing method was changed from random tilting to horizontal or vertical position cor-rection. After those two enhancements, the accuracy of our model increased notably from 97% to 99%. In summary, two subtle methods proposed in this study can remarkably enhance the classification ability of deep learning models in terms of the classification of rice grain.

cs.CV↗

arXiv2025

Trivalent Feynman Diagrams as a Flag

By identifying each standard flag with a trivalent Feynman diagram, the corresponding propagators can be read directly from the flag itself. Within the flag representation, the kinematic Jacobi identity (equivalently, the residue theorem on moduli spaces) admits a natural interpretation as the equivalence between a complete flag and its gapped counterpart. Using flags together with Orlik-Solomon algebras, we reconstruct the intersection numbers of twisted cocycles, thereby obtaining the bi-adjoint amplitude. Moreover, employing flag simplices enables the construction of the Z-amplitude in the alpha' -> 0 limit. By further examining pairings of specific flags, we also recover the Cachazo-He-Yuan (CHY) representation of the bi-adjoint amplitude.

hep-th↗

arXiv2025

LRTuckerRep: Low-rank Tucker Representation Model for Multi-dimensional Data Completion

Multi-dimensional data completion is a critical problem in computational sciences, particularly in domains such as computer vision, signal processing, and scientific computing. Existing methods typically leverage either global low-rank approximations or local smoothness regularization, but each suffers from notable limitations: low-rank methods are computationally expensive and may disrupt intrinsic data structures, while smoothness-based approaches often require extensive manual parameter tuning and exhibit poor generalization. In this paper, we propose a novel Low-Rank Tucker Representation (LRTuckerRep) model that unifies global and local prior modeling within a Tucker decomposition. Specifically, LRTuckerRep encodes low rankness through a self-adaptive weighted nuclear norm on the factor matrices and a sparse Tucker core, while capturing smoothness via a parameter-free Laplacian-based regularization on the factor spaces. To efficiently solve the resulting nonconvex optimization problem, we develop two iterative algorithms with provable convergence guarantees. Extensive experiments on multi-dimensional image inpainting and traffic data imputation demonstrate that LRTuckerRep achieves superior completion accuracy and robustness under high missing rates compared to baselines.

cs.LG↗

arXiv2025

Induced Berry Connection and Photonic Spin Hall Effect in Optical Dirac Theory

Within the framework of optical Dirac theory, we present a field-theoretical model of spin-orbit interaction and photonic spin/orbit Hall effects. Our approach reformulates light propagation along helical paths as solving the Maxwell equations in a ray-based curvilinear coordinate system. This system can alternatively be interpreted as a spin-degenerate medium with antisymmetric elements in the dielectric tensor, corresponding to the spin-1 excitation mode characterized by a rotational dipole vector. We show that, at leading order, the resulting effective Hamiltonian is equivalent to the Maxwell theory in a uniformly rotating frame, incorporating both spin-rotation and orbit-rotation coupling. This rotation arises from the torsion of helical paths, manifesting as extrinsic orbital angular momentum (EOAM) of photons. Notably, the spin angular momentum (SAM) of the photon and its intrinsic orbital angular momentum (IOAM) contribute jointly to the geometric phase. In the Heisenberg picture, the spin and orbital Hall effects naturally emerge from interaction terms in the ray equations. Furthermore, we find that the transverse spin of evanescent waves couples with EOAM, revealing that the geometric phase of elliptically polarized light differs from that of circularly polarized light. This distinction underscores the role of spin-orbit coupling in modifying phase accumulation.

physics.optics↗

arXiv2025

3DeepRep: 3D Deep Low-rank Tensor Representation for Hyperspectral Image Inpainting

Recent approaches based on transform-based tensor nuclear norm (TNN) have demonstrated notable effectiveness in hyperspectral image (HSI) inpainting by leveraging low-rank structures in latent representations. Recent developments incorporate deep transforms to improve low-rank tensor representation; however, existing approaches typically restrict the transform to the spectral mode, neglecting low-rank properties along other tensor modes. In this paper, we propose a novel 3-directional deep low-rank tensor representation (3DeepRep) model, which performs deep nonlinear transforms along all three modes of the HSI tensor. To enforce low-rankness, the model minimizes the nuclear norms of mode-i frontal slices in the corresponding latent space for each direction (i=1,2,3), forming a 3-directional TNN regularization. The outputs from the three directional branches are subsequently fused via a learnable aggregation module to produce the final result. An efficient gradient-based optimization algorithm is developed to solve the model in a self-supervised manner. Extensive experiments on real-world HSI datasets demonstrate that the proposed method achieves superior inpainting performance compared to existing state-of-the-art techniques, both qualitatively and quantitatively.

cs.CV↗

arXiv2025

A proposed deep sea Neutrino Observatory in the Nanhai

Over the past ten years, several breakthroughs have been made in multi-messenger astronomy. Thanks to the IceCube Neutrino Observatory, the detection of astrophysical neutrinos was proved to be practical. However, due to the limited statistics and field of view, only a few sources have been associated with IceCube neutrinos, making new and larger neutrino telescopes necessary. We propose the NEutrino Observatory in the Nanhai (NEON), located in the South China Sea to be complementary for the global neutrino detectors. This proposal describes the design and layout of the array and reports on comprehensive simulations conducted to assess its performance. The NEON project, with a volume of 10 km$^3$, achieves an angular resolution of 0.1$^\circ$ at 100 TeV. With 10 years of operation, the project's 5$σ$ sensitivity is estimated as $E^2Φ\sim 2 \times 10^{-10}$ GeV cm$^{-2}$ s$^{-1}$ for a source spectrum index of -2. We found that the variation in depth from 1700 to 3500 meters does not significantly influence the sensitivity to steady sources.

astro-ph.HE↗

arXiv2025

The traveling-PWN modeling attempt on the enigmatic LHAASO dumbbell-like structure

The first LHAASO catalog presents six enigmatic ultra-high-energy (UHE) gamma-ray sources with lonely > 25 TeV emission being detected, which are indicated as 1LHAASO: J0007+5659u, J0206+4302u, J0212+4254u, J0216+4237u, J1740+0948u, and J1959+1129u. No counterparts of the six sources have been observed, except two energetic pulsars, PSR J0218+4232 and PSR J1740+1000. Among them, 1LHAASO: J0206+4302u, J0212+4254u, and J0216+4237u are connected on the significance map and constituted a dumbbell-like structure. They are close in position and show a similar spectral shape, suggesting a physical association among them. To explain the origin of the six LHAASO sources, especially the intriguing dumbbell-like structure, we conducted the leptonic and hadronic modeling research according to our multiwavelength and multimessenger study. For the dumbbell-like structure, models with traveling-PWNe were considered. The multiwavelength and multimessenger study was based on the Fermi-LAT, Swift-XRT, Planck, CfA 12CO survey, and IceCube neutrino datasets. In the traveling-PWN modeling research, we assumed an isotropic and homogeneous diffusion condition and discussed the influence of diffusion coefficient, distance, and proper motion velocity. No counterparts are discovered in our multiwavelength and multimessenger study, except the two known pulsars. The traveling-PWN modeling attempt with a single PWN appears implausible to explain the dumbbell-like structure, as the diffusion coefficient needs to be much lower than the Bohm limit. A double traveling-PWNe model is also explored and can account for the results of LHAASO-KM2A observation. However, the probability of occurrence of this explanation is significantly lower than that of a conventional triple PWNe explanation.

astro-ph.HE↗

arXiv2025

ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language Models

With the rapid development of Large language models (LLMs), understanding the capabilities of LLMs in identifying unsafe content has become increasingly important. While previous works have introduced several benchmarks to evaluate the safety risk of LLMs, the community still has a limited understanding of current LLMs' capability to recognize illegal and unsafe content in Chinese contexts. In this work, we present a Chinese safety benchmark (ChineseSafe) to facilitate research on the content safety of large language models. To align with the regulations for Chinese Internet content moderation, our ChineseSafe contains 205,034 examples across 4 classes and 10 sub-classes of safety issues. For Chinese contexts, we add several special types of illegal content: political sensitivity, pornography, and variant/homophonic words. Moreover, we employ two methods to evaluate the legal risks of popular LLMs, including open-sourced models and APIs. The results reveal that many LLMs exhibit vulnerability to certain types of safety issues, leading to legal risks in China. Our work provides a guideline for developers and researchers to facilitate the safety of LLMs. Our results are also available at https://huggingface.co/spaces/SUSTech/ChineseSafe-Benchmark. Additionally, we release a test set comprising 200,000 examples, which is publicly accessible at https://huggingface.co/datasets/SUSTech/ChineseSafe.

cs.CL↗

arXiv2025

SNR G54.1+0.3, a PeVatron candidate unveiled by LHAASO

Recently, the LHAASO Collaboration reported the first very-high-energy gamma-ray catalog, containing 90 TeV sources. Among these sources, 1LHAASO J1929+1846u is located 0.3$^\circ$ west of SNR G54.1+0.3 and also lies within a $+53 \, \text{km s}^{-1}$ cloud (the Western Cloud). Moreover, one of the IceCube track-type high-energy starting events is found around 1.3$^\circ$ north of 1LHAASO J1929+1846u, which may serve as strong evidence for the hadronic origin of this TeV source. SNR G54.1+0.3 is a young supernova remnant (SNR), with a powerful pulsar wind nebula (PWN) inside. Its X-ray radiation from the PWN and the SNR Shell can be clearly identified. The radio emission from the PWN region is also given. However, given the angular resolution of gamma-ray experiments, the entire SNR region is viewed as a point source by Fermi-LAT, H.E.S.S. and VERITAS. In this work, we explore a hybrid scenario where SNR G54.1+0.3 is indeed associated with the Western Cloud, and we derive the multi-wavelength emissions from the PWN, the SNR Shell, and the Western Cloud, separately. Our model can explain the observations well, indicating that SNR G54.1+0.3 might be an excellent candidate of Galactic PeVatron and neutrino source.

astro-ph.HE↗

arXiv2025

FrustumFusionNets: A Three-Dimensional Object Detection Network Based on Tractor Road Scene

To address the issues of the existing frustum-based methods' underutilization of image information in road three-dimensional object detection as well as the lack of research on agricultural scenes, we constructed an object detection dataset using an 80-line Light Detection And Ranging (LiDAR) and a camera in a complex tractor road scene and proposed a new network called FrustumFusionNets (FFNets). Initially, we utilize the results of image-based two-dimensional object detection to narrow down the search region in the three-dimensional space of the point cloud. Next, we introduce a Gaussian mask to enhance the point cloud information. Then, we extract the features from the frustum point cloud and the crop image using the point cloud feature extraction pipeline and the image feature extraction pipeline, respectively. Finally, we concatenate and fuse the data features from both modalities to achieve three-dimensional object detection. Experiments demonstrate that on the constructed test set of tractor road data, the FrustumFusionNetv2 achieves 82.28% and 95.68% accuracy in the three-dimensional object detection of the two main road objects, cars and people, respectively. This performance is 1.83% and 2.33% better than the original model. It offers a hybrid fusion-based multi-object, high-precision, real-time three-dimensional object detection technique for unmanned agricultural machines in tractor road scenarios. On the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) Benchmark Suite validation set, the FrustumFusionNetv2 also demonstrates significant superiority in detecting road pedestrian objects compared with other frustum-based three-dimensional object detection methods.

cs.CV↗

arXiv2025

Radio Observation of the Pulsar Wind Nebula in SNR G11.2-0.3

Pulsar wind nebulae (PWNe) are important sources for understanding galactic high-energy processes, but it is controversial until now about how high-energy particles in PWNe are accelerated and transported. Lacking radio counterparts of X-ray PWNe (the proposed acceleration sites) introduce difficulties to better understandings in multi wavelengths. Our recent 3, 6, and 16\,cm high-resolution observations of G11.2$-$0.3 PWN with the Australia Telescope Compact Array (ATCA) uniquely show morphological similarity with its X-ray PWN (a torus/jet feature). Spectral indices of the radio torus and jet are around -0.09 and -0.10, respectively. Meanwhile for the jet region, the spectral break between radio and X-ray spectra implies particle acceleration mechanisms other than a diffusive shock acceleration. Polarization results suggest a helical B-field inside the jet, the equipartition B-field strength of which is below 100\,$μ$G.

astro-ph.HE↗

arXiv2024

SNR G54.1+0.3, a PeVatron candidate unveiled by LHAASO

Recently, the LHAASO Collaboration presented the first very-high-energy gamma-ray catalog, containing 90 TeV sources. Among these sources, 1LHAASO J1929 +1846u* is located 0.3$^\circ$ west of SNR G54.1 +0.3 and it also lies inside a $+53 \, \text{km s}^{-1}$ cloud (the Western Cloud) which may be associate with SNR G54.1+0.3. Moreover, one of IceCube's HESE track events is found at 1.3$^\circ$ north of 1LHAASO J1929 +1846u*. SNR G54.1+0.3 is young, with a powerful PWN inside. The X-ray radiation from the regions of SNR shell and PWN can be distinguished clearly. The radio emission from the PWN region is also available. However, due to the low angular resolution, the gamma-ray emission at the SNR by Fermi, HESS and VERITAS are considered as point sources. In this work, we explore a scenario that SNR G54.1 +0.3 is indeed associated with the Western Cloud and we derive the emissions from the PWN, the SNR shell, and the nearby molecular cloud. Our results can explain the multi-messenger observations, indicating that 1LHAASO J1929 +1846u* might be the excellent candidate of Galactic PeVatron.

astro-ph.HE↗

arXiv2024

The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 38th International Cosmic Ray Conference (ICRC 2023)

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of autonomous radio-detection units to look for the radio emission from extensive air showers that are triggered by the interaction of ultra-high-energy cosmic rays, gamma rays, and neutrinos in the atmosphere or underground. In particular, for ultra-high-energy neutrinos, the future final phase of GRAND aims to be sensitive enough to discover them in spite of their plausibly tiny flux. Presently, three prototype GRAND radio arrays are in operation: GRANDProto300, in China, GRAND@Auger, in Argentina, and GRAND@Nancay, in France. Their goals are to field-test the design of the radio-detection units, understand the radio background to which they are exposed, and develop tools for diagnostic, data gathering, and data analysis. This list of contributions to the 38th International Cosmic Ray Conference (ICRC 2023) presents an overview of GRAND, in its present and future incarnations, and a look at the first data collected by GRANDProto13, the first phase of GRANDProto300.

hep-ex↗

arXiv2023

Status of Women in Astronomy: A need for advancing inclusivity and equal opportunities

Women in the Astronomy and STEM fields face systemic inequalities throughout their careers. Raising awareness, supported by detailed statistical data, represents the initial step toward closely monitoring hurdles in career progress and addressing underlying barriers to workplace equality. This, in turn, contributes to rectifying gender imbalances in STEM careers. The International Astronomical Union Women in Astronomy (IAU WiA) working group, a part of the IAU Executive Committee, is dedicated to increasing awareness of the status of women in Astronomy and supporting the aspirations of female astronomers globally. Its mission includes taking concrete actions to advance equal opportunities for both women and men in the field of astronomy. In August 2021, the IAU WiA Working Group established a new organizing committee, unveiling a comprehensive four-point plan. This plan aims to strengthen various aspects of the group's mission, encompassing: (i) Awareness Sustainability: Achieved through surveys and data collection, (ii) Training and Skill Building: Focused on professional development, (iii) Fundraising: To support key initiatives, and (iv) Communication: Dissemination of results through conferences, WG Magazines, newsletters, and more. This publication provides an overview of focused surveys that illuminate the factors influencing the careers of women in Astronomy, with a particular focus on the careers of mothers. It highlights the lack of inclusive policies, equal opportunities, and funding support for women researchers in the field. Finally, we summarize the specific initiatives undertaken by the IAU WiA Working Group to advance inclusivity and equal opportunities in Astronomy.

astro-ph.IM↗

arXiv2023

Concept-free Causal Disentanglement with Variational Graph Auto-Encoder

In disentangled representation learning, the goal is to achieve a compact representation that consists of all interpretable generative factors in the observational data. Learning disentangled representations for graphs becomes increasingly important as graph data rapidly grows. Existing approaches often rely on Variational Auto-Encoder (VAE) or its causal structure learning-based refinement, which suffer from sub-optimality in VAEs due to the independence factor assumption and unavailability of concept labels, respectively. In this paper, we propose an unsupervised solution, dubbed concept-free causal disentanglement, built on a theoretically provable tight upper bound approximating the optimal factor. This results in an SCM-like causal structure modeling that directly learns concept structures from data. Based on this idea, we propose Concept-free Causal VGAE (CCVGAE) by incorporating a novel causal disentanglement layer into Variational Graph Auto-Encoder. Furthermore, we prove concept consistency under our concept-free causal disentanglement framework, hence employing it to enhance the meta-learning framework, called concept-free causal Meta-Graph (CC-Meta-Graph). We conduct extensive experiments to demonstrate the superiority of the proposed models: CCVGAE and CC-Meta-Graph, reaching up to $29\%$ and $11\%$ absolute improvements over baselines in terms of AUC, respectively.

cs.LG↗