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Gang Li

Publications and source records attributed to Gang Li.

At least 325 records · Page 18Linked to original sources

Variability of magnetic hot stars from the TESS observations

Magnetic hot stars refer to the stars, which effective temperatures approximately in the range from 7,000 to 50,000 K, and with large-scale globally organized magnetic fields. These magnetic fields exhibit strengths ranging from tens of Gauss to tens of kilo-Gauss. They are key in understanding the effects caused by magnetic fields in the stellar evolution. However, there are only three magnetic hot stars studied via a combination of spectropolarimetric and asteroseismic modeling. Combined with $Transiting\;Exoplanet\;Survey\;Satellite\;(TESS)$ 1-56 sectors data sets, we provided a photometric variability and stochastic low frequency (SLF) variability study of 118 magnetic hot stars. 9 new rotating variable stars are identified. Using the Bayesian Markov Chain Monte Carlo (MCMC) framework, we fitted the morphologies of SLF variability for magnetic hot stars. Our analysis reveals that the magnetic hot stars in our sample have $γ< 5.5$ with the vast majority having $1 \leq γ\leq 3$. The $ν_{\rm char}$ is primarily in the ranges of $0\;\text{d}^{-1} < ν_{\rm char} < 6.3\;\text{d}^{-1}$. The amplitude of SLF variability, log$α_{\rm 0}$, shows a dominant distribution ranging from 0.8 to 3. No significant correlations are observed between the luminosity and fitting parameters, suggesting no clear dependence of SLF variability on stellar mass for our sample of magnetic hot stars with masses between approximately $1.5 M_{\odot}< M < 20 M_{\odot}$. We found a significant negative correlation between the $B_{\rm p}$ and $ν_{char}$. This suppression effect of magnetic fields on $ν_{\rm char}$ may be a result of their inhibition of macroturbulence.

astro-ph.SR↗

Nonreciprocal photon transport in indirectly coupled whispering-gallery mode resonators

We study the reflection and transmission properties of a system comprising two whispering-gallery mode resonators, each containing a Zeeman-split quantum dot and side-coupled to an optical fiber. Our results demonstrate that unidirectional reflection and transmission can be achieved by tuning the coupling strength between the resonators and the optical fiber. Furthermore, we establish a correspondence between quantum dot energy level resonance frequencies and the positions of low reflection (transmission) peaks at a phase shift of π. This research provides insights for the development of quantum optical devices like isolators, circulators, and routers.

quant-ph↗

Correlated flat bands in the paramagnetic phase of triangular antiferromagnets Na$_2$BaX(PO$_4$)$_2$ (X = Mn, Co, Ni)

Flat band systems in condensed matter physics are intriguing because they can exhibit exotic phases and unconventional properties. In this work, we studied three correlated magnetic systems, Na$_2$BaX(PO$_4$)$_2$ (X = Mn, Co, Ni), and revealed their unusual electronic structure and magnetic properties. Despite their different effective angular momentum, our first-principles calculations showed a similar electronic structure among them. However, their different valence configurations led to different responses to electronic correlations in the high-temperature paramagnetic phase. Using the dynamical mean-field method, we found that all systems can be understood as a multi-band Hubbard model with Hund'ss coupling. Our calculations of spin susceptibility and the {\it ab-initio} estimation of magnetic exchange coupling indicated strong intra-plane antiferromagnetic coupling and weak inter-plane coupling in all systems. The ground states of these systems are largely degenerate. It is likely that none of these magnetic states would dominate over the others, leading to the possibility of quantum spin liquid states in these systems. Our work unifies the understanding of these three structurally similar systems and opens new avenues for exploring correlated flat bands with distinct electronic and magnetic responses.

cond-mat.str-el↗

MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images

For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer degraded CD performance on small changed areas due to the simple single-scale integration of deep CNNs and transformer modules. To address this issue, we propose a hybrid network based on multi-scale CNN-transformer structure, termed MCTNet, where the multi-scale global and local information are exploited to enhance the robustness of the CD performance on changed areas with different sizes. Especially, we design the ConvTrans block to adaptively aggregate global features from transformer modules and local features from CNN layers, which provides abundant global-local features with different scales. Experimental results demonstrate that our MCTNet achieves better detection performance than existing state-of-the-art CD methods.

cs.CV↗

Study on Virtual Gear Hobbing Simulation and Gear Tooth Surface Accuracy

This paper presents a digital simulation method for the hobbing process of cylindrical gears. Based on the gear generation principle, taking the professional software as the tool, the problem of virtual hobbing simulation on involute helical gears was studied, and the virtual hobbing simulation of hobbing on the whole gear was completed by using macros of CATIA V5. The validity of this method was validated by analyzing the tooth surface accuracy error of the model which was below 0.001 mm between the virtual tooth surface and the theoretical tooth surface and the possible factors that affected the tooth surface accuracy during manufacturing were also carried on the discussion. It offers a fictitious three-D platform for studying the principle of manufacture errors of a gear-cutting machine as well as the finite element analysis between the ideal tooth surface and the erroneous tooth surface.

cs.GR↗

The East-West Asymmetry of Particle Intensity in Energetic Storm Particle Events

We examine the East-West asymmetry of the peak intensity in energetic storm particle (ESP) events using the improved Particle Acceleration and Transport in the Heliosphere (iPATH) model. We find that injection efficiency peaks east of the nose of coronal mass ejection shock where the shock exhibits a quasi-parallel geometry. We show that the peak intensity at the eastern flank is generally larger than that at the western flank and it positively correlates with the injection efficiency. We also examine this asymmetry for heavy ions, which depends sensitively on the ion energy. Comparison between the modelling results with the measurements of ESP events at 1 au shows a reasonable agreement. We suggest that the injection efficiency can be a primary factor leading to the East-West asymmetry of the peak intensity in ESP events. Additionally, the charge-to-mass (Q/A) dependence of the maximum particle energy affects this asymmetry for heavy ions.

astro-ph.SR↗

Performance Analysis and Evaluation of Post Quantum Secure Blockchained Federated Learning

Post-quantum security is critical in the quantum era. Quantum computers, along with quantum algorithms, make the standard cryptography based on RSA or ECDSA over FL or Blockchain vulnerable. The implementation of post-quantum cryptography (PQC) over such systems is poorly understood as PQC is still in its standardization phase. In this work, we propose a hybrid approach to employ PQC over blockchain-based FL (BFL), where we combine a stateless signature scheme like Dilithium (or Falcon) with a stateful hash-based signature scheme like the extended Merkle Signature Scheme (XMSS). We propose a linear-based formulaic approach to device role selection mechanisms based on multiple factors to address the performance aspect. Our holistic approach of utilizing a verifiable random function (VRF) to assist in the blockchain consensus mechanism shows the practicality of the proposed approaches. The proposed method and extensive experimental results contribute to enhancing the security and performance aspects of BFL systems.

cs.CR↗

Improving Federated Aggregation with Deep Unfolding Networks

The performance of Federated learning (FL) is negatively affected by device differences and statistical characteristics between participating clients. To address this issue, we introduce a deep unfolding network (DUN)-based technique that learns adaptive weights that unbiasedly ameliorate the adverse impacts of heterogeneity. The proposed method demonstrates impressive accuracy and quality-aware aggregation. Furthermore, it evaluated the best-weighted normalization approach to define less computational power on the aggregation method. The numerical experiments in this study demonstrate the effectiveness of this approach and provide insights into the interpretability of the unbiased weights learned. By incorporating unbiased weights into the model, the proposed approach effectively addresses quality-aware aggregation under the heterogeneity of the participating clients and the FL environment. Codes and details are \href{https://github.com/shanikairoshi/Improved_DUN_basedFL_Aggregation}{here}.

cs.LG↗

Quantum Federated Learning: Analysis, Design and Implementation Challenges

Quantum Federated Learning (QFL) has gained significant attention due to quantum computing and machine learning advancements. As the demand for QFL continues to surge, there is a pressing need to comprehend its intricacies in distributed environments. This paper aims to provide a comprehensive overview of the current state of QFL, addressing a crucial knowledge gap in the existing literature. We develop ideas for new QFL frameworks, explore diverse use cases of applications, and consider the critical factors influencing their design. The technical contributions and limitations of various QFL research projects are examined while presenting future research directions and open questions for further exploration.

quant-ph↗

PLay: Parametrically Conditioned Layout Generation using Latent Diffusion

Layout design is an important task in various design fields, including user interface, document, and graphic design. As this task requires tedious manual effort by designers, prior works have attempted to automate this process using generative models, but commonly fell short of providing intuitive user controls and achieving design objectives. In this paper, we build a conditional latent diffusion model, PLay, that generates parametrically conditioned layouts in vector graphic space from user-specified guidelines, which are commonly used by designers for representing their design intents in current practices. Our method outperforms prior works across three datasets on metrics including FID and FD-VG, and in user study. Moreover, it brings a novel and interactive experience to professional layout design processes.

cs.LG↗

Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation

With the emerging developments of the Metaverse, a virtual world where people can interact, socialize, play, and conduct their business, it has become critical to ensure that the underlying systems are transparent, secure, and trustworthy. To this end, we develop a decentralized and trustworthy quantum federated learning (QFL) framework. The proposed QFL leverages the power of blockchain to create a secure and transparent system that is robust against cyberattacks and fraud. In addition, the decentralized QFL system addresses the risks associated with a centralized server-based approach. With extensive experiments and analysis, we evaluate classical federated learning (CFL) and QFL in a distributed setting and demonstrate the practicality and benefits of the proposed design. Our theoretical analysis and discussions develop a genuinely decentralized financial system essential for the Metaverse. Furthermore, we present the application of blockchain-based QFL in a hybrid metaverse powered by a metaverse observer and world model. Our implementation details and code are publicly available 1.

cs.LG↗

Insight-HXMT Measurements of the Diffuse X-ray Background

We present an X-ray spectrum of the diffuse X-ray background (DXRB) between 1.5 and 120 keV, as measured with the Low-Energy Detector (LE) and the High-Energy Detector (HE) aboard the Insight-HXMT satellite, based on 'blank-sky' observations. LE covers a nominal energy range of 1-15 keV and HE 20-250 keV, but calibration issues and data quality narrowed the energy range for this work. The LE background was directly measured with `blind' detector modules, while the HE background was derived from Earth-occultation data. With the LE data alone, the measured DXRB spectrum can be well described by a power law; fitting the LE and HE data jointly, however, a spectral cut-off must be introduced in the model to account for the measurements above 30 keV. Modelling the combined spectrum with a cut-off power law, the best-fit photon index is 1.40, normalisation $9.57$~$\rm ph~cm^{-2}~s^{-1}~keV^{-1}~sr^{-1} $ (at 1 keV), and cut-off energy 55 keV, after correcting for the effects of the Earth albedo and atmospheric emission (which are significant in the HE band). Based on the best-fit cut-off power law, we derived the spectral energy distribution (SED) of the DXRB. The shape of the SED is in general agreement with the published measurements, but the overall normalization is lower by varying amounts, except for the HEAO-1 result, with which our result is in good agreement.

astro-ph.HE↗

Influence of the deviation of the matter power spectrum at small scales on the global 21-cm signal at cosmic dawn

The matter power spectrum has been strongly constrained by astronomical measurements at large scales, but only weakly at small scales. Compared with the standard scenario, the deviation of the matter power spectrum at small scales has influence on the cosmological structure formation, e.g., the comoving number density of dark matter halos. The thermal history of the intergalactic medium (IGM) can be changed if dark matter is made of weakly interacting massive particles and can annihilate into standard model particles. The changes of the evolution of IGM could leave imprints on the relevant astronomical observations. Taking into account the dark matter annihilation, we investigate the impact of the deviation of matter power spectrum at small scales on the global 21-cm signal. In view of the measurements of the global 21-cm signal by the EDGES experiment, we explore the allowed parameter space of $m_s$, which describes the degree of deviation, by requiring the differential brightness temperature of the global 21-cm signal $δT_{21} \le -50~\rm mK$ at redshift $z=17$.

astro-ph.CO↗

LibAUC: A Deep Learning Library for X-Risk Optimization

This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-risks. X-risks refer to a family of compositional functions in which the loss function of each data point is defined in a way that contrasts the data point with a large number of others. They have broad applications in AI for solving classical and emerging problems, including but not limited to classification for imbalanced data (CID), learning to rank (LTR), and contrastive learning of representations (CLR). The motivation of developing LibAUC is to address the convergence issues of existing libraries for solving these problems. In particular, existing libraries may not converge or require very large mini-batch sizes in order to attain good performance for these problems, due to the usage of the standard mini-batch technique in the empirical risk minimization (ERM) framework. Our library is for deep X-risk optimization (DXO) that has achieved great success in solving a variety of tasks for CID, LTR and CLR. The contributions of this paper include: (1) It introduces a new mini-batch based pipeline for implementing DXO algorithms, which differs from existing DL pipeline in the design of controlled data samplers and dynamic mini-batch losses; (2) It provides extensive benchmarking experiments for ablation studies and comparison with existing libraries. The LibAUC library features scalable performance for millions of items to be contrasted, faster and better convergence than existing libraries for optimizing X-risks, seamless PyTorch deployment and versatile APIs for various loss optimization. Our library is available to the open source community at https://github.com/Optimization-AI/LibAUC, to facilitate further academic research and industrial applications.

cs.LG↗

Nonlinear optomechanical resonance entering a self-organized energy transfer pattern

The energy transfer between different subsystems or different vibration modes is always one of the most interested problems in the study of the resonance phenomena in coupled nonlinear dynamical systems. With an optomechanical system operating in the regime of unresolved sideband, where its mechanical frequency is lower than the cavity field damping rate, we illustrate the existence of a special nonlinear resonance phenomenon. This type of previously unknown resonance manifests an organized pattern of the coupled cavity field and mechanical oscillation, so that the cavity field precisely pushes the mechanical oscillator within an appropriate small time window in each mechanical oscillation period and the mechanical energy will increase by a jump of almost fixed amount after each oscillation cycle. The scenario is realized at a resonance point where the frequency difference of two driving fields matches the mechanical frequency of the system, and this condition of drive-frequency match is found to trigger a mechanism to lock the two subsystems of an unresolved-sideband optomechanical system into a highly ordered energy transfer as the above mentioned. Due to a significantly enhanced nonlinearity in the vicinity of the resonance point, optical frequency combs can be generated under pump powers of thousand times lower, as compared to the use of a single-tone driving field for the purpose. An unresolved sideband system under the drives without satisfying the resonance condition also demonstrates other interesting dynamical behaviors. Most of all, by providing a realistic picture for the nonlinear optomechanical dynamics in unresolved sideband regime, our study points to a direction to observe novel dynamical phenomena and realize other applications with the systems of less technical restrictions.

physics.optics↗

Ground calibration of Gamma-Ray Detectors of GECAM-C

As a new member of GECAM mission, GECAM-C (also named High Energy Burst Searcher, HEBS) was launched onboard the SATech-01 satellite on July 27th, 2022, which is capable to monitor gamma-ray transients from $\sim$ 6 keV to 6 MeV. As the main detector, there are 12 gamma-ray detectors (GRDs) equipped for GECAM-C. In order to verify the GECAM-C GRD detector performance and to validate the Monte Carlo simulations of detector response, comprehensive on-ground calibration experiments have been performed using X-ray beam and radioactive sources, including Energy-Channel relation, energy resolution, detection efficiency, SiPM voltage-gain relation and the non-uniformity of positional response. In this paper, the detailed calibration campaigns and data analysis results for GECAM-C GRDs are presented, demonstrating the excellent performance of GECAM-C GRD detectors.

astro-ph.IM↗

PaLI-X: On Scaling up a Multilingual Vision and Language Model

We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context) learning, as well as object detection, video question answering, and video captioning. PaLI-X advances the state-of-the-art on most vision-and-language benchmarks considered (25+ of them). Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix.

cs.CV↗

Learning a Single Convolutional Layer Model for Low Light Image Enhancement

Low-light image enhancement (LLIE) aims to improve the illuminance of images due to insufficient light exposure. Recently, various lightweight learning-based LLIE methods have been proposed to handle the challenges of unfavorable prevailing low contrast, low brightness, etc. In this paper, we have streamlined the architecture of the network to the utmost degree. By utilizing the effective structural re-parameterization technique, a single convolutional layer model (SCLM) is proposed that provides global low-light enhancement as the coarsely enhanced results. In addition, we introduce a local adaptation module that learns a set of shared parameters to accomplish local illumination correction to address the issue of varied exposure levels in different image regions. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art LLIE methods in both objective metrics and subjective visual effects. Additionally, our method has fewer parameters and lower inference complexity compared to other learning-based schemes.

cs.CV↗