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Meng Yang

Publications and source records attributed to Meng Yang.

At least 91 records · Page 5Linked to original sources
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↗
arXiv2024

CTC Blank Triggered Dynamic Layer-Skipping for Efficient CTC-based Speech Recognition

Deploying end-to-end speech recognition models with limited computing resources remains challenging, despite their impressive performance. Given the gradual increase in model size and the wide range of model applications, selectively executing model components for different inputs to improve the inference efficiency is of great interest. In this paper, we propose a dynamic layer-skipping method that leverages the CTC blank output from intermediate layers to trigger the skipping of the last few encoder layers for frames with high blank probabilities. Furthermore, we factorize the CTC output distribution and perform knowledge distillation on intermediate layers to reduce computation and improve recognition accuracy. Experimental results show that by utilizing the CTC blank, the encoder layer depth can be adjusted dynamically, resulting in 29% acceleration of the CTC model inference with minor performance degradation.

eess.AS↗
arXiv2023

G2-MonoDepth: A General Framework of Generalized Depth Inference from Monocular RGB+X Data

Monocular depth inference is a fundamental problem for scene perception of robots. Specific robots may be equipped with a camera plus an optional depth sensor of any type and located in various scenes of different scales, whereas recent advances derived multiple individual sub-tasks. It leads to additional burdens to fine-tune models for specific robots and thereby high-cost customization in large-scale industrialization. This paper investigates a unified task of monocular depth inference, which infers high-quality depth maps from all kinds of input raw data from various robots in unseen scenes. A basic benchmark G2-MonoDepth is developed for this task, which comprises four components: (a) a unified data representation RGB+X to accommodate RGB plus raw depth with diverse scene scale/semantics, depth sparsity ([0%, 100%]) and errors (holes/noises/blurs), (b) a novel unified loss to adapt to diverse depth sparsity/errors of input raw data and diverse scales of output scenes, (c) an improved network to well propagate diverse scene scales from input to output, and (d) a data augmentation pipeline to simulate all types of real artifacts in raw depth maps for training. G2-MonoDepth is applied in three sub-tasks including depth estimation, depth completion with different sparsity, and depth enhancement in unseen scenes, and it always outperforms SOTA baselines on both real-world data and synthetic data.

cs.CV↗
arXiv2023

Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification

Convolutional neural networks excel in histopathological image classification, yet their pixel-level focus hampers explainability. Conversely, emerging graph convolutional networks spotlight cell-level features and medical implications. However, limited by their shallowness and suboptimal use of high-dimensional pixel data, GCNs underperform in multi-class histopathological image classification. To make full use of pixel-level and cell-level features dynamically, we propose an asymmetric co-training framework combining a deep graph convolutional network and a convolutional neural network for multi-class histopathological image classification. To improve the explainability of the entire framework by embedding morphological and topological distribution of cells, we build a 14-layer deep graph convolutional network to handle cell graph data. For the further utilization and dynamic interactions between pixel-level and cell-level information, we also design a co-training strategy to integrate the two asymmetric branches. Notably, we collect a private clinically acquired dataset termed LUAD7C, including seven subtypes of lung adenocarcinoma, which is rare and more challenging. We evaluated our approach on the private LUAD7C and public colorectal cancer datasets, showcasing its superior performance, explainability, and generalizability in multi-class histopathological image classification.

cs.CV↗
arXiv2023

FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene

It has long been an ill-posed problem to predict absolute depth maps from single images in real (unseen) indoor scenes. We observe that it is essentially due to not only the scale-ambiguous problem but also the focal-ambiguous problem that decreases the generalization ability of monocular depth estimation. That is, images may be captured by cameras of different focal lengths in scenes of different scales. In this paper, we develop a focal-and-scale depth estimation model to well learn absolute depth maps from single images in unseen indoor scenes. First, a relative depth estimation network is adopted to learn relative depths from single images with diverse scales/semantics. Second, multi-scale features are generated by mapping a single focal length value to focal length features and concatenating them with intermediate features of different scales in relative depth estimation. Finally, relative depths and multi-scale features are jointly fed into an absolute depth estimation network. In addition, a new pipeline is developed to augment the diversity of focal lengths of public datasets, which are often captured with cameras of the same or similar focal lengths. Our model is trained on augmented NYUDv2 and tested on three unseen datasets. Our model considerably improves the generalization ability of depth estimation by 41%/13% (RMSE) with/without data augmentation compared with five recent SOTAs and well alleviates the deformation problem in 3D reconstruction. Notably, our model well maintains the accuracy of depth estimation on original NYUDv2.

cs.CV↗
arXiv2023

Lemniscate ensembles with spectral singularity

We consider a family of random normal matrix models whose eigenvalues tend to occupy lemniscate type droplets as the size of the matrix increases. Under the insertion of a point charge, we derive the scaling limit at the singular boundary point, which is expressed in terms of the solution to the model Painlevé IV Riemann-Hilbert problem. For this, we introduce a version of the Christoffel-Darboux identity and combine it with the strong asymptotics of the associated orthogonal polynomials due to Bertola, Elias Rebelo and Grava.

math.PR↗
arXiv2023

Is Writing Prompts Really Making Art?

In recent years Generative Machine Learning systems have advanced significantly. A current wave of generative systems use text prompts to create complex imagery, video, even 3D datasets. The creators of these systems claim a revolution in bringing creativity and art to anyone who can type a prompt. In this position paper, we question the basis for these claims, dividing our analysis into three areas: the limitations of linguistic descriptions, implications of the dataset, and lastly, matters of materiality and embodiment. We conclude with an analysis of the creative possibilities enabled by prompt-based systems, asking if they can be considered a new artistic medium.

cs.CY↗
arXiv2022

Dimensionality-confined superconductivity within SrNbO3-SrTiO3 heterostructures

Interfaces between transition-metal oxides are able to host two-dimensional electron gases (2DEGs) and exhibit exotic quantum phenomena. Here we report the observation of superconductivity below 230 mK for the heterostructure composed of SrNbO3 (SNO) and SrTiO3 (STO). Different from some other counterparts with two insulators, the metallic SNO provides a novel mechanism to form a quasi 2DEG by charge transfer from bulk towards interface under strain. The superconductivity, residing within the strained SNO layer near the interface, is contributed by an electron system with record-low carrier density. Notably, although embedded in a normal metallic layer with a carrier density 4 to 5 orders higher, the electron system is still uniquely well-protected to retain high mobility and lies deep in extreme quantum regime.

cond-mat.supr-con↗
arXiv2022

A revisit of superconductivity in 4$H_b$-TaS$_{2-2x}$Se$_{2x}$ single crystals

Previous investigations of 4$H_b$-TaS$_{2-2x}$Se$_{2x}$ mainly focused on the direct competition between superconductivity and charge density wave (CDW). However, the superconductivity itself, although has been prominently enhanced by isovalent Se substitution, has not been adequately investigated. Here, we performed a detailed electrical transport measurement down to 0.1 K on a series of 4$H_b$-TaS$_{2-2x}$Se$_{2x}$ single crystals. A systematic fitting of the temperature-dependent resistance demonstrates that the decreased Debye temperatures ($Θ_{D}$) and higher electron-phonon coupling constants ($λ_{e-p}$) at the optimal Se doping content raise the superconducting transition temperature ($T_c$). Additionally, we discovered that the incorporation of Se diminishes the degree of anisotropy of the superconductivity in the highly layered structure. More prominently, a comprehensive analysis of the vortex liquid phase region reveals that the optimally doped sample deviates from the canonical 2D Tinkham prediction but favors a linear trend with the variation of the external magnetic field. These findings emphasize the importance of interlayer interaction in this segregated superconducting-Mott-insulating system.

cond-mat.supr-con↗
arXiv2022

Determinantal Coulomb gas ensembles with a class of discrete rotational symmetric potentials

We consider determinantal Coulomb gas ensembles with a class of discrete rotational symmetric potentials whose droplets consist of several disconnected components. Under the insertion of a point charge at the origin, we derive the asymptotic behaviour of the correlation kernels both in the macro- and microscopic scales. In the macroscopic scale, this particularly shows that there are strong correlations among the particles on the boundary of the droplets. In the microscopic scale, this establishes the edge universality. For the proofs, we use the nonlinear steepest descent method on the matrix Riemann-Hilbert problem to derive the asymptotic behaviours of the associated planar orthogonal polynomials and their norms up to the first subleading terms.

math-ph↗
arXiv2022

Giant and Reversible Electronic Structure Evolution in a Magnetic Topological Material EuCd2As2

The electronic structure and the physical properties of quantum materials can be significantly altered by charge carrier doping and magnetic state transition. Here we report a discovery of a giant and reversible electronic structure evolution with doping in a magnetic topological material. By performing high-resolution angle-resolved photoemission measurements on EuCd2As2,we found that a huge amount of hole doping can be introduced into the sample surface due to surface absorption. The electronic structure exhibits a dramatic change with the hole doping which can not be described by a rigid band shift. Prominent band splitting is observed at high doping which corresponds to a doping-induced magnetic transition at low temperature (below -15 K) from an antiferromagnetic state to a ferromagnetic state. These results have established a detailed electronic phase diagram of EuCd2As2 where the electronic structure and the magnetic structure change systematically and dramatically with the doping level. They further suggest that the transport, magnetic and topological properties of EuCd2As2 can be greatly modified by doping. These work will stimulate further investigations to explore for new phenomena and properties in doping this magnetic topological material.

cond-mat.mtrl-sci↗
arXiv2022

Evidence for electronic signature of magnetic transition in topological magnet HoSbTe

Topological insulators with intrinsic magnetic order are emerging as an exciting platform to realize fundamentally new excitations from topological quantum states of matter. To study these systems and their physics, people have proposed a variety of magnetic topological insulator systems, including HoSbTe, an antiferromagnetic weak topological insulator candidate. In this work, we use scanning tunneling microscopy to probe the electronic structure of HoSbTe with antiferromagnetic and ferromagnetic orders that are tuned by applying an external magnetic field. Although around the Fermi energy, we find minor differences between the quasi-particle interferences under the ferromagnetic and antiferromagnetic orders, deep inside the valance region, a new quasi-particle interference signal emerges with ferromagnetism. This observation is consistent with our first-principles calculations indicating the magnetism-driven transition of the electronic states in this spin-orbit coupled topological magnet.

cond-mat.str-el↗
arXiv2022

Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos

Ultrasound examination is widely used in the clinical diagnosis of thyroid nodules (benign/malignant). However, the accuracy relies heavily on radiologist experience. Although deep learning techniques have been investigated for thyroid nodules recognition. Current solutions are mainly based on static ultrasound images, with limited temporal information used and inconsistent with clinical diagnosis. This paper proposes a novel method for the automated recognition of thyroid nodules through an exhaustive exploration of ultrasound videos and key-frames. We first propose a detection-localization framework to automatically identify the clinical key-frame with a typical nodule in each ultrasound video. Based on the localized key-frame, we develop a key-frame guided video classification model for thyroid nodule recognition. Besides, we introduce a motion attention module to help the network focus on significant frames in an ultrasound video, which is consistent with clinical diagnosis. The proposed thyroid nodule recognition framework is validated on clinically collected ultrasound videos, demonstrating superior performance compared with other state-of-the-art methods.

cs.CV↗
arXiv2022

On the robustness of triaxial Schwarzschild modelling: The effects of correcting the orbit mirroring

In the past 15 years, the triaxial Schwarzschild orbit-superposition code by van den Bosch et al. (2008) has been widely applied to study the dynamics of galaxies. Recently, Quenneville et al. (2022) reported a bug in the orbit calculation of this code, specifically in the mirroring procedure that is used to speed up the computation. We have fixed the incorrect mirroring in DYNAMITE, which is the publicly-released successor of the triaxial Schwarzschild code by van den Bosch et al. (2008). In this study, we provide a thorough quantification of how this bug has affected the results of dynamical analyses performed with this code. We compare results obtained with the original and corrected versions of DYNAMITE, and discuss the differences in the phase-space distribution of a single orbit and in the global stellar orbit distribution, in the mass estimate of the central black hole in the highly triaxial galaxy PGC 46832, and in the measurement of intrinsic shape and enclosed mass for more than 50 galaxies. Focusing on the typical scientific applications of a Schwarzschild triaxial code, in all our tests we find that differences are negligible with respect to the statistical and systematic uncertainties. We conclude that previous results with the van den Bosch et al. (2008) triaxial Schwarzschild code are not significantly affected by the incorrect mirroring.

astro-ph.GA↗
arXiv2022

Leaning Compact and Representative Features for Cross-Modality Person Re-Identification

This paper pays close attention to the cross-modality visible-infrared person re-identification (VI Re-ID) task, which aims to match pedestrian samples between visible and infrared modes. In order to reduce the modality-discrepancy between samples from different cameras, most existing works usually use constraints based on Euclidean metric. Because of the Euclidean based distance metric strategy cannot effectively measure the internal angles between the embedded vectors, the existing solutions cannot learn the angularly discriminative feature embedding. Since the most important factor affecting the classification task based on embedding vector is whether there is an angularly discriminative feature space, in this paper, we present a new loss function called Enumerate Angular Triplet (EAT) loss. Also, motivated by the knowledge distillation, to narrow down the features between different modalities before feature embedding, we further present a novel Cross-Modality Knowledge Distillation (CMKD) loss. Benefit from the above two considerations, the embedded features are discriminative enough in a way to tackle modality-discrepancy problem. The experimental results on RegDB and SYSU-MM01 datasets have demonstrated that the proposed method is superior to the other most advanced methods in terms of impressive performance. Code is available at https://github.com/IVIPLab/LCCRF.

cs.CV↗
arXiv2022

Different Formation Scenarios of Counter-rotating Stellar Disks in Nearby Galaxies

Using the integral field unit (IFU) data from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, we select a sample of 101 galaxies with counter-rotating stellar disks and regularly-rotating ionized gas disk. We classify the 101 galaxies into four types based on the features of their stellar velocity fields. The relative fractions and stellar population age radial gradients of the four types are different in blue cloud (BC), green valley (GV) and red sequence (RS) populations. We suggest different formation scenarios for the counter-rotating stellar disks, and the key factors in the formation of counter-rotating stellar disks include: (1) the abundance of pre-existing gas in progenitor, (2) the efficiency in angular momentum consumption.

astro-ph.GA↗