arXiv subjects

Shuai Liu

Publications and source records attributed to Shuai Liu.

At least 109 records · Page 6Linked to original sources

arXiv2025

Flaring Activities of Fast Rotating Stars have Solar-like Latitudinal Distribution

The dynamo theory has always been one of the biggest mysteries in stellar physics. One key reason for its uncertainty is poor knowledge of the dynamo process on stars except the Sun. The most important observation feature of solar dynamo is that active regions only appear at low latitudes, which provides a crucial constraint to the dynamo theory, while Doppler imaging, the current technique to spatially resolve stellar hemisphere, is difficult to distinguish the equatorial region . Hence, the latitudinal distribution of active regions (LDAR) of stars is ambiguous and controversial, mainly due to the limit of the current technique for spatially resolving the stellar surface. Fast rotating stars, which are young and active, are thought to operate with a different dynamo process than the Sun. We study their LDAR and compare them with the Sun to reveal the underlying dynamo process. Flares are drastic and observational activity events, which occur in active regions. Here, we propose a new method to study how the apparent flaring activity varies with respect to the inclination to determine the LDAR of fast rotating stars.We find that the LDAR of fast rotating stars is consistent with that of the Sun, contrary to expectations. Our results provide a crucial constraint to stellar dynamo, indicating that the solar-like dynamo also applies to fast rotating stars, even spanning different stages of their evolution.

astro-ph.SR↗

arXiv2025

Distributed Observer for Descriptor Linear System: The Luenberger Observer Method

This paper concerns the distributed observer for the descriptor linear system. Unlike centralized descriptor system observers, in the case of distributed observers, each agent either finds it difficult to independently eliminate impulses, or the observer dynamics after eliminating pulses cannot be implemented. To overcome this issue, this paper develops the structure of the distributed observer in two different scenarios, and the observer parameters are presented through a novel design. Moreover, we provide two implementation methods for distributed observer in different scenarios. As a result, each local observer has the ability to reconstruct the states of the underlying system, including its impulse phenomenon. Finally, simulation results verify the validity of our results.

math.DS↗

arXiv2024

Entangled dual-comb spectroscopy

Optical frequency combs have emerged as a cornerstone for a wide range of areas, including spectroscopy, ranging, optical clocks, time and frequency transfer, waveform synthesis, and communications. However, quantum mechanical fluctuations of the optical carrier impose fundamental performance limits on the precision of traditional classical laser frequency combs, particularly in their use for interferometry and spectroscopy. Entanglement, as a quintessential quantum resource, allows for surpassing the fundamental limits of classical systems. Here, we introduce and experimentally demonstrate entangled dual-comb spectroscopy (EDCS) that surmounts the fundamental limits of classical DCS. EDCS builds on tailored entangled spectral structures of the frequency combs, enabling simultaneous detection of all comb lines below the standard quantum limit of classical DCS. Applying EDCS in gas detection, we achieve a 2.6 dB enhancement in signal-to-noise ratio and a 1.7-fold reduction in integration time over classical DCS, rendering EDCS particularly suited for dynamic chemical and biological sensing, where fast, precise measurements subject to power constraints are required. EDCS represents a new paradigm for quantum frequency combs, underscoring their prospects in a plethora of applications in precision metrology, spectroscopy, and timekeeping.

quant-ph↗

arXiv2024

Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation

Text-to-audio (TTA) model is capable of generating diverse audio from textual prompts. However, most mainstream TTA models, which predominantly rely on Mel-spectrograms, still face challenges in producing audio with rich content. The intricate details and texture required in Mel-spectrograms for such audio often surpass the models' capacity, leading to outputs that are blurred or lack coherence. In this paper, we begin by investigating the critical role of U-Net in Mel-spectrogram generation. Our analysis shows that in U-Net structure, high-frequency components in skip-connections and the backbone influence texture and detail, while low-frequency components in the backbone are critical for the diffusion denoising process. We further propose ``Mel-Refine'', a plug-and-play approach that enhances Mel-spectrogram texture and detail by adjusting different component weights during inference. Our method requires no additional training or fine-tuning and is fully compatible with any diffusion-based TTA architecture. Experimental results show that our approach boosts performance metrics of the latest TTA model Tango2 by 25\%, demonstrating its effectiveness.

cs.SD↗

arXiv2024

Fabrication of Ultra-Low-Loss, Dispersion-Engineered Silicon Nitride Photonic Integrated Circuits via Silicon Hardmask Etching

Silicon nitride (Si$_3$N$_4$) photonic integrated circuits (PICs) have emerged as a versatile platform for a wide range of applications, such as nonlinear optics, narrow-linewidth lasers, and quantum photonics. While thin-film Si$_3$N$_4$ processes have been extensively developed, many nonlinear and quantum optics applications require the use of thick Si$_3$N$_4$ films with engineered dispersion, high mode confinement, and low optical loss. However, high tensile stress in thick Si$_3$N$_4$ films often leads to cracking, making the fabrication challenging to meet these requirements. In this work, we present a robust and reliable fabrication method for ultra-low-loss, dispersion-engineered Si$_3$N$_4$ PICs using amorphous silicon (a-Si) hardmask etching. This approach enables smooth etching of thick Si$_3$N$_4$ waveguides while ensuring long-term storage of crack-free Si$_3$N$_4$ wafers. We achieve intrinsic quality factors ($Q_i$) as high as $25.6 \times 10^6$, corresponding to a propagation loss of 1.6 dB/m. The introduction of a-Si hardmask etching and novel crack-isolation trenches offers notable advantages, including high etching selectivity, long-term wafer storage, high yield, and full compatibility with existing well-developed silicon-based semiconductor processes. We demonstrate frequency comb generation in the fabricated microring resonators, showcasing the platform's potential for applications in optical communication, nonlinear optics, metrology, and spectroscopy. This stable and efficient fabrication method offers high performance with significantly reduced fabrication complexity, representing a remarkable advancement toward mass production of Si$_3$N$_4$ PICs for a wide spectrum of applications.

physics.optics↗

arXiv2024

Octopus: Embodied Vision-Language Programmer from Environmental Feedback

Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works either output detailed action sequences at the manipulation level or only provide plans at an abstract level, leaving a gap between high-level planning and real-world manipulation. To bridge this gap, we introduce Octopus, an embodied vision-language programmer that uses executable code generation as a medium to connect planning and manipulation. Octopus is designed to 1) proficiently comprehend an agent's visual and textual task objectives, 2) formulate intricate action sequences, and 3) generate executable code. To facilitate Octopus model development, we introduce OctoVerse: a suite of environments tailored for benchmarking vision-based code generators on a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games such as Grand Theft Auto (GTA) and Minecraft. To train Octopus, we leverage GPT-4 to control an explorative agent that generates training data, i.e., action blueprints and corresponding executable code. We also collect feedback that enables an enhanced training scheme called Reinforcement Learning with Environmental Feedback (RLEF). Through a series of experiments, we demonstrate Octopus's functionality and present compelling results, showing that the proposed RLEF refines the agent's decision-making. By open-sourcing our simulation environments, dataset, and model architecture, we aspire to ignite further innovation and foster collaborative applications within the broader embodied AI community.

cs.CV↗

arXiv2024

Cefdet: Cognitive Effectiveness Network Based on Fuzzy Inference for Action Detection

Action detection and understanding provide the foundation for the generation and interaction of multimedia content. However, existing methods mainly focus on constructing complex relational inference networks, overlooking the judgment of detection effectiveness. Moreover, these methods frequently generate detection results with cognitive abnormalities. To solve the above problems, this study proposes a cognitive effectiveness network based on fuzzy inference (Cefdet), which introduces the concept of "cognition-based detection" to simulate human cognition. First, a fuzzy-driven cognitive effectiveness evaluation module (FCM) is established to introduce fuzzy inference into action detection. FCM is combined with human action features to simulate the cognition-based detection process, which clearly locates the position of frames with cognitive abnormalities. Then, a fuzzy cognitive update strategy (FCS) is proposed based on the FCM, which utilizes fuzzy logic to re-detect the cognition-based detection results and effectively update the results with cognitive abnormalities. Experimental results demonstrate that Cefdet exhibits superior performance against several mainstream algorithms on the public datasets, validating its effectiveness and superiority. Code is available at https://github.com/12sakura/Cefdet.

cs.CV↗

arXiv2024

Chemical abundances of 20 barium stars from the OHP spectra

Based on the high resolution and high signal-to-noise spectra, we derived the chemical abundances of 20 elements for 20 barium (Ba-) stars. For the first time, the detailed abundances of four sample stars, namely HD 92482, HD 150430, HD 151101 and HD 177304 have been analyzed. Additionally, Ba element abundance has been measured using high resolution spectra for the first time in six of the other 16 sample stars. Based on the [s/Fe] ratios, the Ba-unknown star HD 115927 can be classified as a strong Ba-star, while the Ba-likely star HD 160538 can be categorized into a mild Ba-star. Consequently, our sample comprises three strong and 17 mild Ba-stars. The light odd-Z metal elements and Fe-peak elements exhibit near-solar abundances. The [α/Fe] ratios demonstrate decreasing trends with increasing metallicity. Moreover, the abundances of n-capture elements show significant enhancements in different degrees. Using a threshold of the signed distances to the solar r-process abundance pattern ds = 0.6, we find that all of our sample stars are normal Ba-stars, indicating that the enhancements of s-process elements should be attributed to material transfer from their companions. We compare the observed n-capture patterns of sample stars with the FRUITY models, and estimate the mass of the Thermally-Pulsing Asymptotic Giant Branch stars that previously contaminated the Ba-stars. The models with low masses can successfully explain the observations. From a kinematic point of view, we note that most of our sample stars are linked with the thin disk, while HD 130255 may be associated with the thick disk.

astro-ph.SR↗

arXiv2024

Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits

We prove that single-parameter natural exponential families with subexponential tails are self-concordant with polynomial-sized parameters. For subgaussian natural exponential families we establish an exact characterization of the growth rate of the self-concordance parameter. Applying these findings to bandits allows us to fill gaps in the literature: We show that optimistic algorithms for generalized linear bandits enjoy regret bounds that are both second-order (scale with the variance of the optimal arm's reward distribution) and free of an exponential dependence on the bound of the problem parameter in the leading term. To the best of our knowledge, ours is the first regret bound for generalized linear bandits with subexponential tails, broadening the class of problems to include Poisson, exponential and gamma bandits.

cs.LG↗

arXiv2024

Disco4D: Disentangled 4D Human Generation and Animation from a Single Image

We present \textbf{Disco4D}, a novel Gaussian Splatting framework for 4D human generation and animation from a single image. Different from existing methods, Disco4D distinctively disentangles clothings (with Gaussian models) from the human body (with SMPL-X model), significantly enhancing the generation details and flexibility. It has the following technical innovations. \textbf{1)} Disco4D learns to efficiently fit the clothing Gaussians over the SMPL-X Gaussians. \textbf{2)} It adopts diffusion models to enhance the 3D generation process, \textit{e.g.}, modeling occluded parts not visible in the input image. \textbf{3)} It learns an identity encoding for each clothing Gaussian to facilitate the separation and extraction of clothing assets. Furthermore, Disco4D naturally supports 4D human animation with vivid dynamics. Extensive experiments demonstrate the superiority of Disco4D on 4D human generation and animation tasks. Our visualizations can be found in \url{https://disco-4d.github.io/}.

cs.CV↗

arXiv2024

Distributed Online Bandit Nonconvex Optimization with One-Point Residual Feedback via Dynamic Regret

This paper considers the distributed online bandit optimization problem with nonconvex loss functions over a time-varying digraph. This problem can be viewed as a repeated game between a group of online players and an adversary. At each round, each player selects a decision from the constraint set, and then the adversary assigns an arbitrary, possibly nonconvex, loss function to this player. Only the loss value at the current round, rather than the entire loss function or any other information (e.g. gradient), is privately revealed to the player. Players aim to minimize a sequence of global loss functions, which are the sum of local losses. We observe that traditional multi-point bandit algorithms are unsuitable for online optimization, where the data for the loss function are not all a priori, while the one-point bandit algorithms suffer from poor regret guarantees. To address these issues, we propose a novel one-point residual feedback distributed online algorithm. This algorithm estimates the gradient using residuals from two points, effectively reducing the regret bound while maintaining $\mathcal{O}(1)$ sampling complexity per iteration. We employ a rigorous metric, dynamic regret, to evaluate the algorithm's performance. By appropriately selecting the step size and smoothing parameters, we demonstrate that the expected dynamic regret of our algorithm is comparable to existing algorithms that use two-point feedback, provided the deviation in the objective function sequence and the path length of the minimization grows sublinearly. Finally, we validate the effectiveness of the proposed algorithm through numerical simulations.

cs.LG↗

arXiv2024

Chromospheric modeling of the active M3V star G 80-21 with RH1.5D

This study investigates the active regions of the M3.0V star G 80-21 using the observed data from the CARMENES project with synthetic spectra generated by the RH1.5D radiative transfer code. The CARMENES project aims to search for exoplanets around M dwarfs using high-resolution near-infrared and optical echelle spectrographs. By comparing the observed data and models for the chromospheric lines of H$_α$ and the bluest Ca II infrared triplet line, we obtain the best-fit models for this star. The optimal fitting for the observed spectrum of G 80-21 is achieved by employing two active areas in conjunction with an inactive regions, with a calcium abundance of [Ca/H] = $-$0.4. This combination successfully fits all the observed data across varying ratios. The minor active component consistently comprises approximately 18\% of the total (ranging from 14\% to 20\%), which suggests that the minor active component is likely located in the polar regions. Meanwhile, the major active component occupies a variable proportion, ranging from 51\% to 82\%. Our method allows for the determination of the structure and size of stellar chromospheric active regions by analyzing high-resolution observed spectra.

astro-ph.SR↗

arXiv2024

Deep learning for cosmological parameter inference from a dark matter halo density field

We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000 realizations of a cubic box with a side length of 1000 $h^{-1}{\rm Mpc}$, and interpolated over a cubic grid of $300^3$ voxels, with each simulation produced using $512^3$ DM particles and $512^3$ neutrinos. Under the flat $Λ$CDM model, simulations vary standard six cosmological parameters including $Ω_m$, $Ω_b$, $h$, $n_s$, $σ_8$, $w$, along with the neutrino mass sum, $M_ν$. We find that: 1) within the framework of lCNN, extracting large-scale structure information is more efficient from the halo density field compared to relying on the statistical quantities including the power spectrum, the two-point correlation function, and the coefficients from wavelet scattering transform; 2) combining the halo density field with its Fourier transformed counterpart enhances predictions, while augmenting the training dataset with measured statistics further improves performance; 3) achieving high accuracy in inferring $Ω_m$, $h$, and $σ_8$ by the neural network model, while being inefficient in predicting $Ω_b$, { $n_s$}, $M_ν$ and $w$; 4) { compared to the simple fully connected network trained with three statistical quantities, our CNN yields statistically reduced errors, showing improvements of approximately 23\% for $Ω_m$, 11\% for $h$, 8\% for $n_s$, and 21\% for $σ_8$. Additionally, in comparison with the likelihood-based analysis on $P(k)$ data, our CNN provides much tighter constraints on parameters, especially on $Ω_m$ and $σ_8$.} Our study emphasizes this lCNN-based novel approach in extracting large-scale structure information and estimating cosmological parameters.

astro-ph.CO↗

arXiv2024

Discovery of an Extremely r-process-enhanced Thin-disk Star with [Eu/H] = +0.78

Highly r-process-enhanced stars are rare and usually metal-poor ([Fe/H] < - 1.0), and mainly populate the Milky Way halo and dwarf galaxies. This study presents the discovery of a relatively bright (V = 12.72), highly r-process-enhanced (r-II) star ([Eu/Fe] = +1.32, [Ba/Eu] = - 0.95), LAMOST J020632.21 + 494127.9. This star was selected from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution (R ~ 7500) spectroscopic survey; follow-up high-resolution (R ~ 25,000) observations were conducted with the High Optical Resolution Spectrograph (HORuS) installed on the Gran Telescopio Canarias (GTC). The stellar parameters (${T_{\rm eff}}$ = 4130 K, $\rm log\,g $ = 1.52, $ \rm[Fe/H] $ = $ - $0.54, $ξ$ = 1.80 $ \rm{km\,{s^{-1}}} $) have been inferred taking into account non-local thermodynamic equilibrium (NLTE) effects. The abundances of [Ce/Fe], [Pr/Fe], and [Nd/Fe] are +0.19, +0.65 and +0.64, respectively, relatively low compared to the Solar r-process pattern normalized to Eu. This star has a high metallicity ([Fe/H] = - 0.54) compared to most other highly r-process-enhanced stars, and has the highest measured abundance ratio of Eu to H ([Eu/H] = +0.78). It is classified as a thin-disk star based on its kinematics, and does not appear to belong to any known stream or dwarf galaxy.

astro-ph.SR↗

arXiv2024

Merging Hierarchical Triple Black Hole Systems with Intermediate-mass Black Holes in Population III Star Clusters

Theoretical predictions suggest that very massive stars have the potential to form through multiple collisions and eventually evolve into intermediate-mass black holes (IMBHs) within Population III star clusters embedded in mini dark matter haloes. In this study, we investigate the long-term evolution of Population III star clusters, including models with a primordial binary fraction of $f_{\rm b}=0$ and 1, using the $N$-body simulation code PETAR. We comprehensively examine the phenomenon of hierarchical triple black holes in the clusters, specifically focusing on their merging inner binary black holes (BBHs), with post-Newtonian correction, by using the TSUNAMI code. Our findings suggest a high likelihood of the inner BBHs containing IMBHs with masses on the order of $\mathcal{O}(100)M_{\odot}$, and as a result, their merger rate could be up to $0.1{\rm Gpc}^{-3}{\rm yr}^{-3}$. The orbital eccentricities of some merging inner BBHs oscillate over time periodically, known as the Kozai-Lidov oscillation, due to dynamical perturbations. Detectable merging inner BBHs for mHz GW detectors LISA/TianQin/Taiji concentrate within $z<3$. More distant sources would be detectable for CE/ET/LIGO/KAGRA/DECIGO, which are sensitive from $\mathcal{O}(0.1)$Hz to $\mathcal{O}(100)$Hz. Furthermore, compared with merging isolated BBHs, merging inner BBHs affected by dynamical perturbations from tertiary BHs tend to have higher eccentricities, with a significant fraction of sources with eccentricities closing to 1 at mHz bands. GW observations would help constrain formation channels of merging BBHs, whether through isolated evolution or dynamical interaction, by examining eccentricities.

astro-ph.GA↗

arXiv2024

MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation

With the rising demand for high-resolution (HR) images, No-Reference Image Quality Assessment (NR-IQA) gains more attention, as it can ecaluate image quality in real-time on mobile devices and enhance user experience. However, existing NR-IQA methods often resize or crop the HR images into small resolution, which leads to a loss of important details. And most of them are of high computational complexity, which hinders their application on mobile devices due to limited computational resources. To address these challenges, we propose MobileIQA, a novel approach that utilizes lightweight backbones to efficiently assess image quality while preserving image details through high-resolution input. MobileIQA employs the proposed multi-view attention learning (MAL) module to capture diverse opinions, simulating subjective opinions provided by different annotators during the dataset annotation process. The model uses a teacher model to guide the learning of a student model through knowledge distillation. This method significantly reduces computational complexity while maintaining high performance. Experiments demonstrate that MobileIQA outperforms novel IQA methods on evaluation metrics and computational efficiency. The code is available at https://github.com/chencn2020/MobileIQA.

cs.CV↗

arXiv2024

Formation of Quasi-Bound States in the Continuum in a Single Deformed Microcavity

Bound states in the continuum (BIC) holds significant promise in manipulating electromagnetic fields and reducing losses in optical structures, leading to advancements in both fundamental research and practical applications. Despite their observation in various optical systems, the behavior of BIC in whispering-gallery-modes (WGMs) optical microcavities, essential components of photonic integrated chips, has yet to be thoroughly explored. In this study, we propose and experimentally identify a robust mechanism for generating quasi-BIC in a single deformed microcavity. By introducing boundary deformations, we construct stable unidirectional radiation channels as leaking continuum shared by different resonant modes and experimentally verify their external strong mode coupling. This results in drastically suppressed leaking loss of one originally long-lived resonance, manifested as more than a 3-fold enhancement of its quality (Q) factor, while the other short-lived resonance becomes more lossy, demonstrating the formation of Friedrich-Wintgen quasi-BICs as corroborated by both the theoretical model and the experimental data. This research will provide a practical approach to enhance the Q factor of optical microcavities, opening up potential applications in the area of deformed microcavities, nonlinear optics, quantum optics, and integrated photonics.

physics.optics↗

arXiv2024

HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis

In pathological research, education, and clinical practice, the decision-making process based on pathological images is critically important. This significance extends to digital pathology image analysis: its adequacy is demonstrated by the extensive information contained within tissue structures, which is essential for accurate cancer classification and grading. Additionally, its necessity is highlighted by the inherent requirement for interpretability in the conclusions generated by algorithms. For humans, determining tumor type and grade typically involves multi-scale analysis, which presents a significant challenge for AI algorithms. Traditional patch-based methods are inadequate for modeling such complex structures, as they fail to capture the intricate, multi-scale information inherent in whole slide images. Consequently, there is a pressing need for advanced AI techniques capable of efficiently and accurately replicating this complex analytical process. To address this issue, we introduce HistoGym, an open-source reinforcement learning environment for histopathological image analysis. Following OpenAI Gym APIs, HistoGym aims to foster whole slide image diagnosis by mimicking the real-life processes of doctors. Leveraging the pyramid feature of WSIs and the OpenSlide API, HistoGym provides a unified framework for various clinical tasks, including tumor detection and classification. We detail the observation, action, and reward specifications tailored for the histopathological image analysis domain and provide an open-source Python-based interface for both clinicians and researchers. To accommodate different clinical demands, we offer various scenarios for different organs and cancers, including both WSI-based and selected region-based scenarios, showcasing several noteworthy results.

eess.IV↗