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Yong Zhang

Publications and source records attributed to Yong Zhang.

At least 307 records · Page 17Linked to original sources
arXiv2024

A $λ$ 3 mm line survey towards the circumstellar envelope of the carbon-rich AGB star IRC+10216 (CW Leo)

We present an unbiased $λ$ 3 mm spectral line survey (between 84.5 and 115.8 GHz), conducted by the Purple Mountain Observatory 13.7 meter radio telescope, together with updated modeling results, towards the carbon-rich Asymptotic Giant Branch star, IRC+10216 (CW Leo). A total of 75 spectral lines (96 transitions) are detected, and identified to arise from 19 molecules: C$_2$H, $l$-C$_3$H, C$_4$H, CN, C$_3$N, HC$_3$N, HC$_5$N, HCN, HNC, CH$_3$CN, MgNC, CO, $c$-C$_3$H$_2$, SiC$_2$, SiO, SiS, CS, C$_2$S, C$_3$S, and their isotopologues. Among them, one molecular emission line (H$^{13}$CCCN $J=13-12$) is discovered in IRC+10216 for the first time. The excitation temperature, column density, and fractional abundance of the detected species are deduced by assuming they are in local thermodynamic equilibrium. In addition, the isotopic ratios of [$^{12}$C]/[$^{13}$C], [$^{32}$S]/[$^{34}$S], [$^{28}$Si]/[$^{29}$Si], and [$^{12}$C$^{34}$S]/[$^{13}$C$^{32}$S] are obtained and found to be consistent with previous studies. Finally, we summarize all of the 106 species detected in IRC+10216 to date with their observed and modeled column densities for the convenience of future studies.

astro-ph.SR↗
arXiv2024

Squeezed correlations of bosons with nonzero widths for expanding sources

We explore the squeezed back-to-back correlation (SBBC) and investigate how the squeezing effect influences the Hanbury Brown-Twiss (HBT) interferometry using an expanding Gaussian source with non-zero width. The SBBC and HBT of $D^0$ and $ϕ$ mesons with finite in-medium widths are studied. The expanding flow of the source may enhance the SBBC strength of $D^0{\bar D}^0$ and $ϕϕ$ in the low momentum region but suppress the SBBC in the larger momentum region. The squeezing effect suppresses the influence of flow on the HBT radii, which is significant for two identical bosons with large pair momentum or with large mass. Due to the squeezing effect, the relationship between the HBT radii and the pair momentum exhibits non-flow behavior for $D^0D^0$ pair. Likewise, non-flow behavior also appears in the HBT radii of $ϕϕ$ with large pair momentum. This phenomenon may bring new insights for studying the squeezing effect.

hep-ph↗
arXiv2024

Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning

Large Language Models (LLMs) have shown significant promise in various applications, including zero-shot and few-shot learning. However, their performance can be hampered by inherent biases. Instead of traditionally sought methods that aim to minimize or correct these biases, this study introduces a novel methodology named ``bias-kNN''. This approach capitalizes on the biased outputs, harnessing them as primary features for kNN and supplementing with gold labels. Our comprehensive evaluations, spanning diverse domain text classification datasets and different GPT-2 model sizes, indicate the adaptability and efficacy of the ``bias-kNN'' method. Remarkably, this approach not only outperforms conventional in-context learning in few-shot scenarios but also demonstrates robustness across a spectrum of samples, templates and verbalizers. This study, therefore, presents a unique perspective on harnessing biases, transforming them into assets for enhanced model performance.

cs.CL↗
arXiv2024

Machine Learning Insides OptVerse AI Solver: Design Principles and Applications

In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. Furthermore, we introduce a training framework leveraging augmentation policies to maintain solvers' utility in dynamic environments. Besides the data generation and augmentation, our proposed approaches also include novel ML-driven policies for personalized solver strategies, with an emphasis on applications like graph convolutional networks for initial basis selection and reinforcement learning for advanced presolving and cut selection. Additionally, we detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance. Compared with traditional solvers such as Cplex and SCIP, our ML-augmented OptVerse AI Solver demonstrates superior speed and precision across both established benchmarks and real-world scenarios, reinforcing the practical imperative and effectiveness of machine learning techniques in mathematical programming solvers.

cs.AI↗
arXiv2024

VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method, particularly in picture quality, motion, and concept composition.

cs.CV↗
arXiv2024

Intelligence of Astronomical Optical Telescope: Present Status and Future Perspectives

Artificial intelligence technology has been widely used in astronomy, and new artificial intelligence technologies and application scenarios are constantly emerging. There have been a large number of papers reviewing the application of artificial intelligence technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status and research hotspots of telescope intelligence from these papers. This paper combines the development history of artificial intelligence technology and the difficulties of critical technologies of telescopes, comprehensively introduces the development and research hotspots of telescope intelligence, then conducts statistical analysis on various research directions of telescope intelligence and defines the research directions' merits. All kinds of research directions are evaluated, and the research trend of each telescope's intelligence is pointed out. Finally, according to the advantages of artificial intelligence technology and the development trend of telescopes, future research hotspots of telescope intelligence are given.

astro-ph.IM↗
arXiv2023

Gas-phase molecules in protoplanetary nebulae with the 21 μm emission feature

It has been more than 30 years since the enigmatic 21 μm emission feature was first discovered in protoplanetary nebulae (PPNs). Although dozens of different dust carrier candidates have been proposed, there is as yet no widely accepted one. We present the results of molecular observations toward 21μm objects using the 10m Submillimeter Telescope of Arizona Radio Observatory at the 1.3 mm band and the 13.7 m telescope of Purple Mountain Observatory at the 3mm band, aiming to investigate whether the gas-phase environments of these unusual sources have some peculiarities compared to normal PPNs. We detect 31 emission lines belonging to seven different molecular species, most of which are the first detection in 21 μm PPNs. The observations provide clues on the identification of the 21 μm feature. We report a correlation study between the fractional abundance of gas-phase molecules and the strengths of the 21 μm emission. Our study shows that given the small sample size, the 21 μm feature has weak or no correlations with the gas-phase molecules. Future radio observations of high spatial and spectral resolution toward a large sample are desirable to elucidate the 21 μm emission phenomena.

astro-ph.GA↗
arXiv2023

Law of the logarithm for the maximum interpoint distance constructed by high-dimensional random matrix

Suppose $\left \{ X_{i,k}; 1\le i \le p, 1\le k \le n \right \} $ is an array of i.i.d.~real random variables. Let $\left \{ p=p_{n}; n \ge1 \right \} $ be positive integers. Consider the maximum interpoint distance $M_{n}=\max_{1\le i< j\le p} \left \| \boldsymbol{X}_{i}- \boldsymbol{X}_{j} \right \|_{2} $ where $\boldsymbol{X}_{i}$ and $\boldsymbol{X}_{j}$ denote the $i$-th and $j$-th rows of the $p \times n$ matrix $\mathcal{M} _{p,n}=\left( X_{i,k} \right)_{p \times n}$, respectively. This paper shows the laws of the logarithm for $M_{n}$ under two high-dimensional settings: the polynomial rate and the exponential rate. The proofs rely on the moderation deviation principle of the partial sum of i.i.d.~random variables, the Chen--Stein Poisson approximation method and Gaussian approximation.

math.PR↗
arXiv2023

Satellite Impact on Astronomical Observations Based on Elliptical Orbit Model

Space-based and ground-based telescopes have extensively documented the impact of satellites on astronomical observations. With the proliferation of satellite mega-constellation programs, their influence on astronomical observations has become undeniable. It is crucial to quantify the impact of satellites on telescopes. To address this need, we have enhanced the circular orbit model for satellites and introduced a methodology based on two-line element (TLE) orbit data. This involves constructing a satellite probability distribution model to evaluate the impact of satellites on telescopes. Using our method, we assessed the satellite impact on global observatories. The results indicate that the regions most severely affected by satellite interference currently are those near the equator, with latitudes around 50 and 80 degrees experiencing the most significant impact from low Earth orbit satellites. Furthermore, we validated the reliability of our method using imaging data obtained from the focal surface acquisition camera of the LAMOST telescope.

astro-ph.IM↗
arXiv2023

DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization

Despite the impressive results of arbitrary image-guided style transfer methods, text-driven image stylization has recently been proposed for transferring a natural image into a stylized one according to textual descriptions of the target style provided by the user. Unlike the previous image-to-image transfer approaches, text-guided stylization progress provides users with a more precise and intuitive way to express the desired style. However, the huge discrepancy between cross-modal inputs/outputs makes it challenging to conduct text-driven image stylization in a typical feed-forward CNN pipeline. In this paper, we present DiffStyler, a dual diffusion processing architecture to control the balance between the content and style of the diffused results. The cross-modal style information can be easily integrated as guidance during the diffusion process step-by-step. Furthermore, we propose a content image-based learnable noise on which the reverse denoising process is based, enabling the stylization results to better preserve the structure information of the content image. We validate the proposed DiffStyler beyond the baseline methods through extensive qualitative and quantitative experiments. Code is available at \url{https://github.com/haha-lisa/Diffstyler}.

cs.CV↗
arXiv2023

Experimental 3D super-localization with Laguerre-Gaussian modes

Improving three-dimensional (3D) localization precision is of paramount importance for super-resolution imaging. By properly engineering the point spread function (PSF), such as utilizing Laguerre-Gaussian (LG) modes and their superposition, the ultimate limits of 3D localization precision can be enhanced. However, achieving these limits is challenging, as it often involves complicated detection strategies and practical limitations. In this work, we rigorously derive the ultimate 3D localization limits of LG modes and their superposition, specifically rotation modes, in the multi-parameter estimation framework. Our findings reveal that a significant portion of the information required for achieving 3D super-localization of LG modes can be obtained through feasible intensity detection. Moreover, the 3D ultimate precision can be achieved when the azimuthal index $l$ is zero. To provide a proof-of-principle demonstration, we develop an iterative maximum likelihood estimation (MLE) algorithm that converges to the 3D position of a point source, considering the pixelation and detector noise. The experimental implementation exhibits an improvement of up to two-fold in lateral localization precision and up to twenty-fold in axial localization precision when using LG modes compared to Gaussian mode. We also showcase the superior axial localization capability of the rotation mode within the near-focus region, effectively overcoming the limitations encountered by single LG modes. Notably, in the presence of realistic aberration, the algorithm robustly achieves the Cramér-Rao lower bound. Our findings provide valuable insights for evaluating and optimizing the achievable 3D localization precision, which will facilitate the advancements in super-resolution microscopy.

quant-ph↗
arXiv2023

AnimateZero: Video Diffusion Models are Zero-Shot Image Animators

Large-scale text-to-video (T2V) diffusion models have great progress in recent years in terms of visual quality, motion and temporal consistency. However, the generation process is still a black box, where all attributes (e.g., appearance, motion) are learned and generated jointly without precise control ability other than rough text descriptions. Inspired by image animation which decouples the video as one specific appearance with the corresponding motion, we propose AnimateZero to unveil the pre-trained text-to-video diffusion model, i.e., AnimateDiff, and provide more precise appearance and motion control abilities for it. For appearance control, we borrow intermediate latents and their features from the text-to-image (T2I) generation for ensuring the generated first frame is equal to the given generated image. For temporal control, we replace the global temporal attention of the original T2V model with our proposed positional-corrected window attention to ensure other frames align with the first frame well. Empowered by the proposed methods, AnimateZero can successfully control the generating progress without further training. As a zero-shot image animator for given images, AnimateZero also enables multiple new applications, including interactive video generation and real image animation. The detailed experiments demonstrate the effectiveness of the proposed method in both T2V and related applications.

cs.CV↗
arXiv2023

E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation

The recent advancements in Large Language Models (LLMs) have sparked interest in harnessing their potential within recommender systems. Since LLMs are designed for natural language tasks, existing recommendation approaches have predominantly transformed recommendation tasks into open-domain natural language generation tasks. However, this approach necessitates items to possess rich semantic information, often generates out-of-range results, and suffers from notably low efficiency and limited extensibility. Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency. Nevertheless, the incapacity of LLMs to model IDs presents a formidable challenge when seeking to leverage LLMs for personalized recommendations. In this paper, we introduce an Elegant Effective Efficient Extensible solution for large language models for Sequential Recommendation (E4SRec), which seamlessly integrates LLMs with traditional recommender systems that exclusively utilize IDs to represent items. Specifically, E4SRec takes ID sequences as inputs, ensuring that the generated outputs fall within the candidate lists. Furthermore, E4SRec possesses the capability to generate the entire ranking list in a single forward process, and demands only a minimal set of pluggable parameters, which are trained for each dataset while keeping the entire LLM frozen. We substantiate the effectiveness, efficiency, and extensibility of our proposed E4SRec through comprehensive experiments conducted on four widely-used real-world datasets. The implementation code is accessible at https://github.com/HestiaSky/E4SRec/.

cs.IR↗
arXiv2023

DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

Animating a still image offers an engaging visual experience. Traditional image animation techniques mainly focus on animating natural scenes with stochastic dynamics (e.g. clouds and fluid) or domain-specific motions (e.g. human hair or body motions), and thus limits their applicability to more general visual content. To overcome this limitation, we explore the synthesis of dynamic content for open-domain images, converting them into animated videos. The key idea is to utilize the motion prior of text-to-video diffusion models by incorporating the image into the generative process as guidance. Given an image, we first project it into a text-aligned rich context representation space using a query transformer, which facilitates the video model to digest the image content in a compatible fashion. However, some visual details still struggle to be preserved in the resultant videos. To supplement with more precise image information, we further feed the full image to the diffusion model by concatenating it with the initial noises. Experimental results show that our proposed method can produce visually convincing and more logical & natural motions, as well as higher conformity to the input image. Comparative evaluation demonstrates the notable superiority of our approach over existing competitors.

cs.CV↗
arXiv2023

Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks

Combinatorial optimization finds an optimal solution within a discrete set of variables and constraints. The field has seen tremendous progress both in research and industry. With the success of deep learning in the past decade, a recent trend in combinatorial optimization has been to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning (ML) models. In this paper, we investigate two essential aspects of machine learning algorithms for combinatorial optimization: temporal characteristics and attention. We argue that for the task of variable selection in the branch-and-bound (B&B) algorithm, incorporating the temporal information as well as the bipartite graph attention improves the solver's performance. We support our claims with intuitions and numerical results over several standard datasets used in the literature and competitions. Code is available at: https://developer.huaweicloud.com/develop/aigallery/notebook/detail?id=047c6cf2-8463-40d7-b92f-7b2ca998e935

cs.LG↗
arXiv2023

Absence of metallicity and bias-dependent resistivity in low-carrier-density EuCd2As2

EuCd2As2 was theoretically predicted to be a minimal model of Weyl semimetals with a single pair of Weyl points in the ferromagnet state. However, the heavily p-doped EuCd2As2 crystals in previous experiments prevent direct identification of the semimetal hypothesis. Here we present a comprehensive magneto-transport study of high-quality EuCd2As2 crystals with ultralow bulk carrier density (10^13 cm-3). In contrast to the general expectation of a Weyl semimetal phase, EuCd2As2 shows insulating behavior in both antiferromagnetic and ferromagnetic states as well as surface-dominated conduction from band bending. Moreover, the application of a dc bias current can dramatically modulate the resistance by over one order of magnitude, and induce a periodic resistance oscillation due to the geometric resonance. Such nonlinear transport results from the highly nonequilibrium state induced by electrical field near the band edge. Our results suggest an insulating phase in EuCd2As2 and put a strong constraint on the underlying mechanism of anomalous transport properties in this system.

cond-mat.mes-hall↗
arXiv2023

The Ground State Lattice Distortion of CsV$_{3}$Sb$_{5}$ Revealed by de Haas-van Alphen Oscillations

The recently discovered AV$_{3}$Sb$_{5}$ (A = K, Rb, Cs) compounds have garnered intense attention in the scientific community due to their unique characteristics as kagome superconductors coexisting with a charge density wave (CDW) order. To comprehend the electronic properties of this system, it is essential to understand the lattice distortions associated with the CDW order and the ground state electronic behavior. Here, we comprehensively examine the Fermi surface by a combination of angle-dependent torque magnetometry and density functional theory calculations. We observe magnetic breakdown in the de Haas-van Alphen oscillations under high magnetic fields. Additionally, by examining the angular and temperature variations in quantum oscillation frequencies, we gain insight into the evolution of the three-dimensional like Fermi surfaces and the cyclotron masses of the orbits, which are consistent with weak electron-phonon coupling. Notably, further comparisons indicate that the 2$\times$2$\times$2 Star-of-David (SoD) distortion is more compatible with both high frequency data above 1000\,T and low frequency data below 500\,T, while the 2$\times$2$\times$2 Tri-Hexagonal (TrH) distortion aligns well with experimental data at mid frequencies. This finding implies the inherent coexistence of both TrH and SoD 2$\times$2$\times$2 patterns within the CDW order. These observations provide key insights into the interplay among effective electronic dimensionality, CDW state, and superconductivity.

cond-mat.supr-con↗
arXiv2023

Near-Infrared Ca II Triplet As An Stellar Activity Indicator: Library and Comparative Study

We have established and released a new stellar index library of the Ca II Triplet, which serves as an indicator for characterizing the chromospheric activity of stars. The library is based on data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS) Data Release 9 (DR9). To better reflect the chromospheric activity of stars, we have defined new indices $R$ and $R^{+}$. The library includes measurements of $R$ and $R^{+}$ for each Ca II infrared triplet (IRT) from 699,348 spectra of 562,863 F, G and K-type solar-like stars with Signal-to-Noise Ratio (SNR) higher than 100, as well as the stellar atmospheric parameters and basic information inherited from the LAMOST LRS Catalog. We compared the differences between the 3 individual index of the Ca II Triplet and also conducted a comparative analysis of $R^{+}_{\lambda8542}$ to the Ca II H&K $S$ and $R^+_{HK}$ index database. We find the fraction of low active stars decreases with $T_{eff}$ and the fraction of high active first decrease with decreasing temperature and turn to increase with decreasing temperature at 5800K. We also find a significant fraction of stars that show high activity index in both Ca II H&K and IRT are binaries with low activity, some of them could be discriminated in Ca II H&K $S$ index and $R^{+}_{\lambda8542}$ space. This newly stellar library serves as a valuable resource for studying chromospheric activity in stars and can be used to improve our comprehension of stellar magnetic activity and other astrophysical phenomena.

astro-ph.SR↗