arXiv subjects

Li Deng

Publications and source records attributed to Li Deng.

At least 19 recordsLinked to original sources

arXiv2026

Uniform asymptotic stability and regions of attraction for systems on Riemannian manifolds

This paper studies uniform asymptotic stability of equilibrium points of ordinary differential equations on complete Riemannian manifolds and estimates of the corresponding regions of attraction. We first refine the classical Lyapunov stability theorem in Euclidean space and obtain a sharper estimate of the region of attraction. We then extend this result to systems on complete Riemannian manifolds and establish sufficient conditions for uniform asymptotic stability together with an explicit estimate of the corresponding region of attraction. The resulting estimate is constrained by both the injectivity radius at the equilibrium point and the Lyapunov comparison functions. Compared with a previous result, the obtained theorem removes the monotonicity assumption on the squared distance function along system trajectories. An example on the two-dimensional hyperbolic space illustrates the result and shows that this monotonicity condition may fail.

math.DS↗

arXiv2026

Coronal Diagnostics Via Modelling Periodic-Beaded Stripes of Solar Radio Bursts

Using high-resolution data from the Chashan Broadband Solar radio spectrometer at meter wavelengths (CBSm) of the Chinese Meridian Project-Phase II (CMP-II), Li et al. (2025) identified a novel fine spectral structure of solar radio bursts, termed periodic beaded stripes, and proposed a generation mechanism. Here we report additional events and develop a quantitative method to determine the physical conditions in the emission region. Periodic stripes tend to occur in the post-phase of flares and are associated with complex magnetic configurations. They repeat on sub-second timescales and show $\sim$0.1 s bead-like modulations, often accompanied by low-frequency absorptions. Modeling the chained stripes with linear kinetic theory of the double plasma resonance (DPR) instability constrains the source-region magnetic field to 0.2-1.7 G and the plasma density to (1-7) $\times 10^8$ cm $^{-3}$. The former follows the drift of individual stripes, and the latter tracks the overall trend. This study summarizes the key properties of periodic beaded stripes and establishes a quantitative DPR-based framework for coronal diagnostics.

astro-ph.SR↗

arXiv2026

Permutation invariant multi-scale full quantum neural network wavefunction

Solving the intricate quantum behavior of interacting particles is key to unlocking the mysteries of condensed matter, but capturing their complex correlations across different scales remains a monumental challenge. We introduce a neural network framework that overcomes this barrier by modeling the full quantum wavefunction of a system, including electrons, nuclei and muons, directly capturing the full quantum effects beyond the Born-Oppenheimer approximation. The neural network approximates joint wavefunction of different interacting particles with a rigorous handling of permutation invariance, enabling simultaneous treatment of nuclear quantum effects and electron-nucleus-muon couplings without explicit excited states. Validated on molecular systems, this approach offers a computationally feasible way to model full quantum phenomena in complex many-body systems, establishing a direct connection between fundamental particle properties and emergent material behavior.

physics.chem-ph↗

arXiv2026

Lyapunov Stability for nonautonomous systems on Manifolds

This paper studies the uniformly asymptotic stability of nonautonomous systems on Riemannian manifolds. We establish corresponding Lyapunov-type theorems (Theorems 2.1 and 2.2), extending classical Euclidean results (e.g., [9, Theorems 4.9 and 4.10]) to curved spaces. Our main contributions are: (i) an estimate for the domain of attraction linked to the equilibrium point's injectivity radius, where, under suitable conditions, this radius can be bounded using the sectional curvature (Proposition 2.1); (ii) a demonstration that this estimate depends on the choice of the Riemannian metric (Examples 2.1 and 2.2 and Remark 2.4); and (iii) a refined estimate compared to the Euclidean case, as detailed in item (6) of Remark 2.1 and in Remark 2.3.

math.DS↗

arXiv2025

Collaborative Group-Aware Hashing for Fast Recommender Systems

The fast online recommendation is critical for applications with large-scale databases; meanwhile, it is challenging to provide accurate recommendations in sparse scenarios. Hash technique has shown its superiority for speeding up the online recommendation by bit operations on Hamming distance computations. However, existing hashing-based recommendations suffer from low accuracy, especially with sparse settings, due to the limited representation capability of each bit and neglected inherent relations among users and items. To this end, this paper lodges a Collaborative Group-Aware Hashing (CGAH) method for both collaborative filtering (namely CGAH-CF) and content-aware recommendations (namely CGAH) by integrating the inherent group information to alleviate the sparse issue. Firstly, we extract inherent group affinities of users and items by classifying their latent vectors into different groups. Then, the preference is formulated as the inner product of the group affinity and the similarity of hash codes. By learning hash codes with the inherent group information, CGAH obtains more effective hash codes than other discrete methods with sparse interactive data. Extensive experiments on three public datasets show the superior performance of our proposed CGAH and CGAH-CF over the state-of-the-art discrete collaborative filtering methods and discrete content-aware recommendations under different sparse settings.

cs.IR↗

arXiv2025

Detecting FRB by DANCE: a method based on DEnsity ANalysis and Cluster Extraction

Fast radio bursts (FRBs) are transient signals exhibiting diverse strengths and emission bandwidths. Traditional single-pulse search techniques are widely employed for FRB detection; yet weak, narrow-band bursts often remain undetectable due to low signal-to-noise ratios (SNR) in integrated profiles. We developed DANCE, a detection tool based on cluster analysis of the original spectrum. It is specifically designed to detect and isolate weak, narrow-band FRBs, providing direct visual identification of their emission properties. This method performs density clustering on reconstructed, RFI-cleaned observational data, enabling the extraction of targeted clusters in time-frequency domain that correspond to the genuine FRB emission range. Our simulations show that DANCE successfully extracts all true signals with SNR~>5 and achieves a detection precision exceeding 93%. Furthermore, through the practical detection of FRB 20201124A, DANCE has demonstrated a significant advantage in finding previously undetectable weak bursts, particularly those with distinct narrow-band features or occurring in proximity to stronger bursts.

astro-ph.HE↗

arXiv2025

Improving Muon Scattering Tomography Performance With A Muon Momentum Measurement Scheme

Muon imaging, especially muon scattering tomography (MST), has recently garnered significant attention. MST measures the magnitude of muon scattering angles inside an object, which depends not only on the material properties but also on the muon momentum. Due to the difficulty of simultaneous measurement of momentum, it was neglected and taken as a constant in multiple MST reconstruction algorithms. Recently, an experimental measurement scheme has emerged that is feasible in engineering, but it requires many layers of detectors to approach the true momentum. From this, we proposed both an algorithm to incorporating momentum into MST, and a scheme to determine the thresholds of Cherenkov detectors. This novel scheme, termed the "equi-percentage scheme", sets momentum thresholds for Cherenkov detector layers based on cosmic muon momentum distribution. Results showed our approach delivers noticeable enhancement in reconstructed image quality even with only two detector layers, reaching near-saturation performance with four layers. This study proves that momentum measurement significantly enhances short-duration MST, and that substantial improvement can be achieved with relatively coarse momentum measurement using 2-4 layers of Cherenkov detectors.

physics.ins-det↗

arXiv2025

Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering

Metric learning has attracted extensive interest for its ability to provide personalized recommendations based on the importance of observed user-item interactions. Current metric learning methods aim to push negative items away from the corresponding users and positive items by an absolute geometrical distance margin. However, items may come from imbalanced categories with different intra-class variations. Thus, the absolute distance margin may not be ideal for estimating the difference between user preferences over imbalanced items. To this end, we propose a new method, named discrete scale-invariant metric learning (DSIML), by adding binary constraints to users and items, which maps users and items into binary codes of a shared Hamming subspace to speed up the online recommendation. Specifically, we firstly propose a scale-invariant margin based on angles at the negative item points in the shared Hamming subspace. Then, we derive a scale-invariant triple hinge loss based on the margin. To capture more preference difference information, we integrate a pairwise ranking loss into the scale-invariant loss in the proposed model. Due to the difficulty of directly optimizing the mixed integer optimization problem formulated with \textit{log-sum-exp} functions, we seek to optimize its variational quadratic upper bound and learn hash codes with an alternating optimization strategy. Experiments on benchmark datasets clearly show that our proposed method is superior to competitive metric learning and hashing-based baselines for recommender systems.

cs.IR↗

arXiv2025

A Novel Fine Spectral Structure of Solar Radio Bursts with Periodic Beaded Stripes Observed by CBSm of CMP-II

A novel fine spectral structure in solar radio bursts has been discovered using the Chashan broadband solar radio spectrometer at meter wavelengths (CBSm), an instrument of the Chinese Meridian Project-Phase II (CMP-II). The structure features periodic narrow-band stripes with a typical recurrence time $< 1 $ s (occasionally reaches 8 s), often drifting from high to low frequencies and accompanied by absorptions, with trailing stripes appearing at the end of preceding ones. Some stripes exhibit periodic beaded enhancements with a periodicity of $\sim$0.1 s. The beaded stripes are reported for the first time ever. Data from the DAocheng Radio Telescope (DART) indicate a radio emission brightness temperature exceeding $10^{9}$ K, originating above brightening loops in active region AR 13664. We proposed a novel generation mechanism of the periodic stripes on the basis of the double plasma resonance (DPR) instability, and explained the beaded substructure in terms of modulation by low-frequency magnetohydrodynamic (MHD) waves. The study highlights the CBSm's capability to detect high-resolution fine spectral structures and offers novel insights into the emission mechanism and source characteristics of solar radio bursts.

astro-ph.SR↗

arXiv2025

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges

Financial Large Language Models (FinLLMs), such as open FinGPT and proprietary BloombergGPT, have demonstrated great potential in select areas of financial services. Beyond this earlier language-centric approach, Multimodal Financial Foundation Models (MFFMs) can digest interleaved multimodal financial data, including fundamental data, market data, data analytics, macroeconomic, and alternative data (e.g., natural language, audio, images, and video). In this position paper, presented at the MFFM Workshop joined with ACM International Conference on AI in Finance (ICAIF) 2024, we describe the progress, prospects, and challenges of MFFMs. This paper also highlights ongoing research on FinAgents in the \textbf{SecureFinAI Lab}\footnote{\https://openfin.engineering.columbia.edu/} at Columbia University. We believe that MFFMs will enable a deeper understanding of the underlying complexity associated with numerous financial tasks and data, streamlining the operation of financial services and investment processes. Github Repo https://github.com/Open-Finance-Lab/Awesome-MFFMs/.

cs.CE↗

arXiv2025

Radio dimming associated with filament eruptions in the meter and decimeter wavebands

Filament eruptions are considered to be a common phenomenon on the Sun and other stars, yet they are rarely directly imaged in the meter and decimeter wavebands. Using imaging data from the DAocheng solar Radio Telescope (DART) in the 150-450 MHz frequency range, we present two eruptive filaments that manifest as radio dimmings (i.e., emission depressions). Simultaneously, portions of these eruptive filaments are discernible as dark features in the chromospheric images. The sun-as-a-star flux curves of brightness temperature, derived from the DART images, exhibit obvious radio dimmings. The dimming depths range from 1.5% to 8% of the background level and show a negative correlation with radio frequencies and a positive correlation with filament areas. Our investigation suggests that radio dimming is caused by free-free absorption during filament eruptions obscuring the solar corona. This may provide a new method for detecting stellar filament eruptions.

astro-ph.SR↗

arXiv2025

Exploring the origin of multi-periodic pulsations during a white-light flare

We explored the quasi-periodic pulsations (QPPs) at multiple periods during an X4.0 flare on 2024 May 10 (SOL2024-05-10T06:27), which occurred in the complex active region of NOAA 13664. The flare radiation reveals five prominent periods in multiple wavelengths. A 8-min QPP is simultaneously detected in wavelengths of HXR, radio, UV/EUV, Lya, and white light, which may be associated with nonthermal electrons periodically accelerated by intermittent magnetic reconnection that is modulated by the slow wave. A quasi-period at 14 minutes is observed in the SXR and high-temperature EUV wavebands, and it may be caused by repeatedly heated plasmas in hot flare loops. A quasiperiod at about 18 minutes is only observed by STIX, with reconstructed SXR images suggesting that the 18-min period pulsations should be considered as different flares. Meanwhile, a 3-min QPP is simultaneously detected in wavelengths of HXR, radio, and UV/ EUV, which is directly modulated by the slow magnetoacoustic wave leaking from sunspot umbrae. At last, a 2-min QPP is simultaneously detected in HXR and radio emissions during the pre-flare phase, which is possibly generated by a quasi-periodic regime of magnetic reconnection that is triggered by the kink wave.

astro-ph.SR↗

arXiv2025

Transfer learning empowers material Z classification with muon tomography

Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers (Z values), facilitating the identification of various Z-class materials, particularly those radioactive high-Z nuclear elements. Most of the traditional identification methods are based on complex muon event reconstruction and trajectory fitting processes. Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of target materials, significantly limiting their practical applicability in detecting concealed materials. For the first time, transfer learning is introduced into the field of muon tomography in this work. We propose two lightweight neural network models for fine-tuning and adversarial transfer learning, utilizing muon tomography data of bare materials to predict the Z-class of coated materials. By employing the inverse cumulative distribution function method, more accurate scattering angle distributions could be obtained from limited data, leading to an improvement by nearly 4\% in prediction accuracy compared with the traditional random sampling based training. When applied to coated materials with limited labeled or even unlabeled muon tomography data, the proposed method achieves an overall prediction accuracy exceeding 96\%, with high-Z materials reaching nearly 99\%. Simulation results indicate that transfer learning improves prediction accuracy by approximately 10\% compared to direct prediction without transfer. This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data, highlights the promising potential of transfer learning in the field of muon tomography.

physics.ins-det↗

arXiv2025

The Feasibility Study of the GeV-Energy Muon Source Based on HIAF

Generating a mono-energetic, high-energy muon beam using accelerator facilities can be very attractive for many purposes, for example, improving muon tomography currently limited by the low flux and wide energy spread of cosmic ray muons, and searching for muon related new physics beyond the Standard Model. One potential accelerator facility is the High Intensity Heavy-Ion Accelerator Facility (HIAF), which is currently under construction in Huizhou City, China. Considering the projectile energy and beamline length, a high-intensity and GeV-energy muon flux could be produced and delivered by the High Energy Fragment Separator beamline of the HIAF facility. In this paper, the flux intensity and purity of muon beam based on HIAF are discussed in detail. For the $\mu^+$ beam, the highest muon yield reaches $8.2 \times 10^6 ~ \mu$/s with the purity of approximately $2\%$ at a momentum of 3.5 GeV/c; meanwhile, for the $\mu^-$ beam, the maximum muon yield is 4.2 $\times 10^6 ~ \mu$/s with the purity of around $20\%$ at a momentum of 1.5 GeV/c. The results also indicate that, for muon beams with an energy of several GeV, by applying a suitable purification strategy, we can get a muon beam with a purity of 100\% and an intensity of the order of $10^5 ~ \mu$/s.

physics.acc-ph↗

arXiv2025

Second-Order Necessary Optimality Conditions for Multi-Objective Optimal Control Problems on Riemannian Manifolds

In this paper, we investigate the multi-objective optimal control problem of ordinary differential equations on Riemannian manifolds. We first obtain the second-order necessary conditions for weak Pareto optimal solutions for multi-objective optimal control problems with fixed terminal time, and then extend these results to multi-objective optimal control problems with free terminal time, deriving the corresponding second-order necessary conditions for weak Pareto optimal solutions. Our main results (i.e., Theorem 2.2 and Theorem 2.4) show that weak Pareto optimal solutions depend on the curvature tensor of the Riemannian manifold. Finally, we provide an example (i.e., Example 2.1) as an application of our main results, illustrating how our findings differ from existing related results (see Remark 2.2).

math.OC↗

arXiv2025

Open FinLLM Leaderboard: Towards Financial AI Readiness

Financial large language models (FinLLMs) with multimodal capabilities are envisioned to revolutionize applications across business, finance, accounting, and auditing. However, real-world adoption requires robust benchmarks of FinLLMs' and FinAgents' performance. Maintaining an open leaderboard is crucial for encouraging innovative adoption and improving model effectiveness. In collaboration with Linux Foundation and Hugging Face, we create an open FinLLM leaderboard, which serves as an open platform for assessing and comparing AI models' performance on a wide spectrum of financial tasks. By demoncratizing access to advances of financial knowledge and intelligence, a chatbot or agent may enhance the analytical capabilities of the general public to a professional level within a few months of usage. This open leaderboard welcomes contributions from academia, open-source community, industry, and stakeholders. In particular, we encourage contributions of new datasets, tasks, and models for continual update. Through fostering a collaborative and open ecosystem, we seek to promote financial AI readiness.

cs.CE↗

arXiv2024

A 44-minute periodic radio transient in a supernova remnant

Long-period radio transients (LPTs) are a newly discovered class of radio emitters with periods ranging from minutes to hours. The astrophysical nature remains undetermined, particularly of LPTs with no detectable companions. We report the first evidence for a plausible supernova remnant (SNR) association with an LPT (DART J1832-0911, 2656.23+-0.15 s period), which supports a neutron star origin of such objects. The dispersion measure of this LPT, SNR's CO emission and HI absorption, and low probability of chance of alignment with field pulsars are all consistent with such an association. The source displays either phase-locked circular or nearly 100\% linear polarization, indicating its strong and geometrically stable magnetic field. No detectable optical counterpart was found, even with a 10m-class telescope. The SNR association and the stable polarization suggest that DART J1832-0911 most likely originates from a young neutron star, whose spin could have been braked by supernova's fallback materials. This discovery provides critical insights into the nature of ultra-long period transients and their link to stellar remnants.

astro-ph.HE↗

arXiv2024

First Proof of Principle Experiment for Muon Production with Ultrashort High Intensity Laser

Muons, which play a crucial role in both fundamental and applied physics, have traditionally been generated through proton accelerators or from cosmic rays. With the advent of ultra-short high-intensity lasers capable of accelerating electrons to GeV levels, it has become possible to generate muons in laser laboratories. In this work, we show the first proof of principle experiment for novel muon production with an ultra-short, high-intensity laser device through GeV electron beam bombardment on a lead converter target. The muon physical signal is confirmed by measuring its lifetime which is the first clear demonstration of laser-produced muons. Geant4 simulations were employed to investigate the photo-production, electro-production, and Bethe-Heitler processes response for muon generation and their subsequent detection. The results show that the dominant contributions of muons are attributed to the photo-production/electro-production and a significant yield of muons up to 0.01 $\mu$/$e^-$ out of the converter target could be achieved. This laser muon source features compact, ultra-short pulse and high flux. Moreover, its implementation in a small laser laboratory is relatively straightforward, significantly reducing the barriers to entry for research in areas such as muonic X-ray elemental analysis, muon spin spectroscopy and so on.

physics.acc-ph↗