arXiv subjects

Meng Yang

Publications and source records attributed to Meng Yang.

At least 73 records · Page 4Linked to original sources

arXiv2025

A Causality-Inspired Model for Intima-Media Thickening Assessment in Ultrasound Videos

Carotid atherosclerosis represents a significant health risk, with its early diagnosis primarily dependent on ultrasound-based assessments of carotid intima-media thickening. However, during carotid ultrasound screening, significant view variations cause style shifts, impairing content cues related to thickening, such as lumen anatomy, which introduces spurious correlations that hinder assessment. Therefore, we propose a novel causal-inspired method for assessing carotid intima-media thickening in frame-wise ultrasound videos, which focuses on two aspects: eliminating spurious correlations caused by style and enhancing causal content correlations. Specifically, we introduce a novel Spurious Correlation Elimination (SCE) module to remove non-causal style effects by enforcing prediction invariance with style perturbations. Simultaneously, we propose a Causal Equivalence Consolidation (CEC) module to strengthen causal content correlation through adversarial optimization during content randomization. Simultaneously, we design a Causal Transition Augmentation (CTA) module to ensure smooth causal flow by integrating an auxiliary pathway with text prompts and connecting it through contrastive learning. The experimental results on our in-house carotid ultrasound video dataset achieved an accuracy of 86.93\%, demonstrating the superior performance of the proposed method. Code is available at \href{https://github.com/xielaobanyy/causal-imt}{https://github.com/xielaobanyy/causal-imt}.

cs.CV↗

arXiv2025

Room-temperature field-tunable radiofrequency rectification in epitaxial SrIrO3 films

Although significant advancements have been made in wireless technologies and portable devices, it remains a challenge for high-frequency and nanowatt-level radiofrequency rectification. In this work, we report a pronounced radiofrequency rectification up to 37 GHz in nominally centrosymmetric SrIrO3 epitaxial films, with the minimum detectable power as low as ~300 nanowatts. Strikingly, the SrIrO3 rectifier is highly field-tunable and exhibits a strong in-plane field anisotropy, thus showing a unique advantage in broad-band radiofrequency rectification. The rectification effect can persist up to at least 360 K and shows a sensitive temperature dependence including a sign inversion. By a systematic study of the nonlinear transport properties of SrIrO3, it is further revealed that the radiofrequency rectification originates from the nonlinear Hall effect with the dominant contribution from field-induced Berry curvature dipole. Our work demonstrates the superior performance of the field-tunable SrIrO3 rectifiers, unleashing the great application potential of centrosymmetric materials in harvesting and detecting ambient electromagnetic energy.

cond-mat.mtrl-sci↗

arXiv2025

Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound Videos

The intersection of medical imaging and artificial intelligence has become an important research direction in intelligent medical treatment, particularly in the analysis of medical images using deep learning for clinical diagnosis. Despite the advances, existing keyframe classification methods lack extraction of time series features, while ultrasonic video classification based on three-dimensional convolution requires uniform frame numbers across patients, resulting in poor feature extraction efficiency and model classification performance. This study proposes a novel video classification method based on CNN and LSTM, introducing NLP's long and short sentence processing scheme into video classification for the first time. The method reduces CNN-extracted image features to 1x512 dimension, followed by sorting and compressing feature vectors for LSTM training. Specifically, feature vectors are sorted by patient video frame numbers and populated with padding value 0 to form variable batches, with invalid padding values compressed before LSTM training to conserve computing resources. Experimental results demonstrate that our variable-frame CNNLSTM method outperforms other approaches across all metrics, showing improvements of 3-6% in F1 score and 1.5% in specificity compared to keyframe methods. The variable-frame CNNLSTM also achieves better accuracy and precision than equal-frame CNNLSTM. These findings validate the effectiveness of our approach in classifying variable-frame ultrasound videos and suggest potential applications in other medical imaging modalities.

cs.CV↗

arXiv2025

Robust Microgrid Dispatch with Real-Time Energy Sharing and Endogenous Uncertainty

With the rising adoption of distributed energy resources (DERs), microgrid dispatch is facing new challenges: DER owners are independent stakeholders seeking to maximize their individual profits rather than being controlled centrally; and the dispatch of renewable generators may affect the microgrid's exposure to uncertainty. To address these challenges, this paper proposes a two-stage robust microgrid dispatch model with real-time energy sharing and endogenous uncertainty. In the day-ahead stage, the connection/disconnection of renewable generators is optimized, which influences the size and dimension of the uncertainty set. As a result, the uncertainty set is endogenously given. In addition, non-anticipative operational bounds for energy storage (ES) are derived to enable the online operation of ES in real-time. In the real-time stage, DER owners (consumers and prosumers) share energy with each other via a proposed energy sharing mechanism, which forms a generalized Nash game. To solve the robust microgrid dispatch model, we develop an equivalent optimization model to compute the real-time energy sharing equilibrium. Based on this, a projection-based column-and-constraint generation (C&CG) method is proposed to handle the endogenous uncertainty. Numerical experiments show the effectiveness and advantages of the proposed model and method.

math.OC↗

arXiv2025

Free energy of spherical Coulomb gases with point charges

We consider two-dimensional Coulomb gases on the Riemann sphere with determinantal or Pfaffian structures, under external potentials that are invariant under rotations around the axis connecting the north and south poles, and with microscopic point charges inserted at the poles. These models can be interpreted as Coulomb gases on the complex plane with weakly confining potentials, where the associated droplet is the entire complex plane. For these models, we derive precise asymptotic expansions of the free energies, including the constant terms.

math-ph↗

arXiv2024

Local Statistics in Normal Matrix Models with Merging Singularity

We study the normal matrix model, also known as the two-dimensional one-component plasma at a specific temperature, with merging singularity. As the number $n$ of particles tends to infinity we obtain the limiting local correlation kernel at the singularity, which is related to the parametrix of the Painlevé~II equation. The two main tools are Riemann-Hilbert problems and the generalized Christoffel-Darboux identity. The correlation kernel exhibits a novel anisotropic scaling behavior, where the corresponding spacing scale of particles is $n^{-1/3}$ in the direction of merging and $n^{-1/2}$ in the perpendicular direction. In the vicinity at different distances to the merging singularity we also observe Ginibre bulk and edge statistics, as well as the sine-kernel and the universality class corresponding to the elliptic ensemble in the weak non-Hermiticity regime for the local correlation function.

math-ph↗

arXiv2024

New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook

Thanks to the explosive growth of data and the development of computational resources, it is possible to build pre-trained models that can achieve outstanding performance on various tasks, such as neural language processing, computer vision, and more. Despite their powerful capabilities, pre-trained models have also sparked attention to the emerging security challenges associated with their real-world applications. Security and privacy issues, such as leaking privacy information and generating harmful responses, have seriously undermined users' confidence in these powerful models. Concerns are growing as model performance improves dramatically. Researchers are eager to explore the unique security and privacy issues that have emerged, their distinguishing factors, and how to defend against them. However, the current literature lacks a clear taxonomy of emerging attacks and defenses for pre-trained models, which hinders a high-level and comprehensive understanding of these questions. To fill the gap, we conduct a systematical survey on the security risks of pre-trained models, proposing a taxonomy of attack and defense methods based on the accessibility of pre-trained models' input and weights in various security test scenarios. This taxonomy categorizes attacks and defenses into No-Change, Input-Change, and Model-Change approaches. With the taxonomy analysis, we capture the unique security and privacy issues of pre-trained models, categorizing and summarizing existing security issues based on their characteristics. In addition, we offer a timely and comprehensive review of each category's strengths and limitations. Our survey concludes by highlighting potential new research opportunities in the security and privacy of pre-trained models.

cs.AI↗

arXiv2024

Scale Propagation Network for Generalizable Depth Completion

Depth completion, inferring dense depth maps from sparse measurements, is crucial for robust 3D perception. Although deep learning based methods have made tremendous progress in this problem, these models cannot generalize well across different scenes that are unobserved in training, posing a fundamental limitation that yet to be overcome. A careful analysis of existing deep neural network architectures for depth completion, which are largely borrowing from successful backbones for image analysis tasks, reveals that a key design bottleneck actually resides in the conventional normalization layers. These normalization layers are designed, on one hand, to make training more stable, on the other hand, to build more visual invariance across scene scales. However, in depth completion, the scale is actually what we want to robustly estimate in order to better generalize to unseen scenes. To mitigate, we propose a novel scale propagation normalization (SP-Norm) method to propagate scales from input to output, and simultaneously preserve the normalization operator for easy convergence. More specifically, we rescale the input using learned features of a single-layer perceptron from the normalized input, rather than directly normalizing the input as conventional normalization layers. We then develop a new network architecture based on SP-Norm and the ConvNeXt V2 backbone. We explore the composition of various basic blocks and architectures to achieve superior performance and efficient inference for generalizable depth completion. Extensive experiments are conducted on six unseen datasets with various types of sparse depth maps, i.e., randomly sampled 0.1\%/1\%/10\% valid pixels, 4/8/16/32/64-line LiDAR points, and holes from Structured-Light. Our model consistently achieves the best accuracy with faster speed and lower memory when compared to state-of-the-art methods.

cs.CV↗

arXiv2024

Diffusion Driven Transient Hydrogenation in Metal Superhydrides at Extreme Conditions

In recent years, metal hydride research has become one of the driving forces of the high-pressure community, as it is believed to hold the key to superconductivity close to ambient temperature. While numerous novel metal hydride compounds have been reported and extensively investigated for their superconducting properties, little attention has been focused on the atomic and electronic states of hydrogen, the main ingredient in these novel compounds. Here, we present combined $^{1}H$- and $^{139}La$-NMR data on lanthanum superhydrides, $LaH_{x}$, ($x = 10.2 - 11.1$), synthesized after laser heating at pressures above 160 GPa. Strikingly, we found hydrogen to be in a highly diffusive state at room temperature, with diffusion coefficients in the order of $10^{-6}~cm^2s^{-1}$. We found that this diffusive state of hydrogen results in a dynamic de-hydrogenation of the sample over the course of several weeks, approaching a composition similar to its precursor materials. Quantitative measurements demonstrate that the synthesized superhydrides continuously decompose over time. Transport measurements underline this conclusion as superconducting critical temperatures were found to decrease significantly over time as well. This observation sheds new light on formerly unanswered questions on the long-term stability of metal superhydrides.

cond-mat.mtrl-sci↗

arXiv2024

Microsatellite-based real-time quantum key distribution

A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guaranteed by the laws of quantum mechanics. A quantum satellite constellation offers a solution to facilitate the quantum network on a global scale. The Micius satellite has verified the feasibility of satellite quantum communications, however, scaling up quantum satellite constellations is challenging, requiring small lightweight satellites, portable ground stations and real-time secure key exchange. Here we tackle these challenges and report the development of a quantum microsatellite capable of performing space-to-ground QKD using portable ground stations. The quantum microsatellite features a payload weighing approximately 23 kg, while the portable ground station weighs about 100 kg. These weights represent reductions by more than an order and two orders of magnitude, respectively, compared to the Micius satellite. Additionally, we multiplex bidirectional satellite-ground optical communication with quantum communication, enabling key distillation and secure communication in real-time. Using the microsatellite and the portable ground stations, we demonstrate satellite-based QKD with multiple ground stations and achieve the sharing of up to 0.59 million bits of secure keys during a single satellite pass. The compact quantum payload can be readily assembled on existing space stations or small satellites, paving the way for a satellite-constellation-based quantum and classical network for widespread real-life applications.

quant-ph↗

arXiv2024

Hexagonal to Monoclinic Phase Transition in Dense Hydrogen Phase III Detected by High-Pressure NMR

Conclusive crystal structure determination of the high pressure phases of hydrogen remains elusive due to lack of core electrons and vanishing wave vectors, rendering standard high-pressure experimental methods moot. Ab-initio DFT calculations have shown that structural polymorphism might be solely resolvable using high-resolution nuclear magnetic resonance (NMR) spectroscopy at mega-bar pressures, however technical challenges have precluded such experiments thus far. Here, we present in-situ high-pressure high-resolution NMR experiments in hydrogen phase III between 181 GPa and 208 GPa at room temperature. Our spectra suggest that at lower pressures phase III adopts a hexagonal P6122 crystal structure, transitioning into a monoclinic C2/c phase at about 197 GPa. The high resolution spectra are in excellent agreement with earlier structural and spectral predictions and underline the possibility of a subtle P6122 to C2/c phase transition in hydrogen phase III. These experiments show the importance of a combination of ab-initio calculations and low-Z sensitive spectral probes in high-pressure science in elucidating the structural complexity of the most abundant element in our universe.

cond-mat.mtrl-sci↗

arXiv2024

SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching

Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images. Nevertheless, most existing methods depend on precise pixel-level annotations by medical professionals, a process that is both costly and labor-intensive. Recently, segmentation foundation models like SAM have shown impressive generalizability on natural images, suggesting their potential as pseudo-labelers. However, accurate prompts remain crucial for their success in medical images. In this work, we devise a novel weakly supervised framework that effectively utilizes the segmentation foundation model to generate pseudo-labels from aspect ration annotations for automatic nodule segmentation. Specifically, we develop three types of bounding box prompts based on scalable shape priors, followed by an adaptive pseudo-label selection module to fully exploit the prediction capabilities of the foundation model for nodules. We also present a SAM-driven uncertainty-aware cross-teaching strategy. This approach integrates SAM-based uncertainty estimation and label-space perturbations into cross-teaching to mitigate the impact of pseudo-label inaccuracies on model training. Extensive experiments on two clinically collected ultrasound datasets demonstrate the superior performance of our proposed method.

cs.CV↗

arXiv2024

Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition

Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios. Classic end-to-end models fused with extra language models perform well, but mainly in data matching scenarios and are gradually approaching a bottleneck. In this work, we introduce Seed-ASR, a large language model (LLM) based speech recognition model. Seed-ASR is developed based on the framework of audio conditioned LLM (AcLLM), leveraging the capabilities of LLMs by inputting continuous speech representations together with contextual information into the LLM. Through stage-wise large-scale training and the elicitation of context-aware capabilities in LLM, Seed-ASR demonstrates significant improvement over end-to-end models on comprehensive evaluation sets, including multiple domains, accents/dialects and languages. Additionally, Seed-ASR can be further deployed to support specific needs in various scenarios without requiring extra language models. Compared to recently released large ASR models, Seed-ASR achieves 10%-40% reduction in word (or character, for Chinese) error rates on Chinese and English public test sets, further demonstrating its powerful performance.

eess.AS↗

arXiv2024

Exploring Real-Time Music-to-Image Systems for Creative Inspiration in Music Creation

This paper presents a study on the use of a real-time music-to-image system as a mechanism to support and inspire musicians during their creative process. The system takes MIDI messages from a keyboard as input which are then interpreted and analysed using state-of-the-art generative AI models. Based on the perceived emotion and music structure, the system's interpretation is converted into visual imagery that is presented in real-time to musicians. We conducted a user study in which musicians improvised and composed using the system. Our findings show that most musicians found the generated images were a novel mechanism when playing, evidencing the potential of music-to-image systems to inspire and enhance their creative process.

cs.HC↗

arXiv2024

Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation

Recent advances in deep learning have greatly facilitated the automated segmentation of ultrasound images, which is essential for nodule morphological analysis. Nevertheless, most existing methods depend on extensive and precise annotations by domain experts, which are labor-intensive and time-consuming. In this study, we suggest using simple aspect ratio annotations directly from ultrasound clinical diagnoses for automated nodule segmentation. Especially, an asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels, i.e., conservative labels and radical labels, to train two asymmetric segmentation networks simultaneously. Subsequently, a conservative-radical-balance strategy (CRBS) strategy is proposed to complementally combine radical and conservative labels. An inconsistency-aware dynamically mixed pseudo-labels supervision (IDMPS) module is introduced to address the challenges of over-segmentation and under-segmentation caused by the two types of labels. To further leverage the spatial prior knowledge provided by clinical annotations, we also present a novel loss function namely the clinical anatomy prior loss. Extensive experiments on two clinically collected ultrasound datasets (thyroid and breast) demonstrate the superior performance of our proposed method, which can achieve comparable and even better performance than fully supervised methods using ground truth annotations.

cs.CV↗

arXiv2024

Korevaar-Schoen spaces on Sierpiński carpets

We prove that certain $L^p$-regularity functional inequality holds on \emph{generalized Sierpiński carpets}. This gives an affirmative answer to an open question raised by Fabrice Baudoin. Our technique originates from an old idea of Alf Jonsson in 1996.

math.FA↗

arXiv2024

Dark matter measurements combining stellar and HI kinematics: 30% $1-σ$ outliers with low dark matter content at $5R_\mathrm{e}$

We construct the Schwarzschild dynamical models for 11 early-type galaxies with the SAURON and Mitchell stellar IFUs out to $2-4 R_\mathrm{e}$, and construct dynamical models with combined stellar and HI kinematics for a subsample of 4 galaxies with HI velocity fields out to $10 R_\mathrm{e}$ obtained from the Westerbork Synthesis Radio Telescope, thus robustly obtaining the dark matter content out to large radii for these galaxies. Adopting a generalised-NFW dark matter profile, we measure an NFW-like density cusp in the dark matter inner slopes for all sample galaxies, with a mean value of $1.00\pm0.04$ (rms scatter $0.15$). The mean dark matter fraction for the sample is $0.2$ within $1 R_\mathrm{e}$, and increases to $0.4$ at $2 R_\mathrm{e}$, and $0.6$ at $5 R_\mathrm{e}$. The dark matter fractions within $1 R_\mathrm{e}$ of these galaxies are systematically lower than the predictions of both the TNG-100 and EAGLE simulations. For the dark matter fractions within $2 R_\mathrm{e}$ and $5 R_\mathrm{e}$, 40% and 70% galaxies are $1-σ$ consistent with either the TNG-100 or the EAGLE predictions, while the remaining 60% and 30% galaxies lie below the $1-σ$ region. Combined with 36 galaxies with dark matter fractions measured out to $5 R_\mathrm{e}$ in the literature, about 10% of these 47 galaxies lie below the $3-σ$ region of the TNG-100 or EAGLE predictions.

astro-ph.GA↗

arXiv2024

Uncovering the formation of the counter-rotating stellar disks in SDSS J074834.64+444117.8

Using the integral field spectroscopic data from Mapping Nearby Galaxies at Apache Point Observatory survey, we study the kinematics and stellar population properties of the two counter-rotating stellar disks in a nearby galaxy SDSS J074834.64+444117.8. We disentangle the two stellar disks by three methods, including CaII $λ$8542 double Gaussian fit, pPXF spectral decomposition, and orbit-based dynamical model. These three different methods give consistent stellar kinematics. The pPXF spectral decomposition provides the spectra of two stellar disks, with one being more luminous across the whole galaxy named primary disk, and the other named secondary disk. The primary disk is counter-rotating with ionized gas, while the secondary disk is co-rotating with ionized gas. The secondary disk has younger stellar population and poorer stellar metallicity than the primary disk. We estimate the stellar mass ratio between the primary and secondary disks to be $\sim$5.2. The DESI $g$, $r$, $z$ color image doesn't show any merger remnant feature in this galaxy. These findings support a scenario that the counter-rotating stellar disks in SDSS J074834.64+444117.8 formed through gas accretion from the cosmic web or a gas-rich companion.

astro-ph.GA↗