arXiv subjects

Yan Yang

Publications and source records attributed to Yan Yang.

At least 109 records · Page 6Linked to original sources

arXiv2023

Some results on the maximal chromatic polynomials of $2$-connected $k$-chromatic graphs

In 2015, Brown and Erey conjectured that every $2$-connected graph $G$ on $n$ vertices with chromatic number $k\geq 4$ has at most $(x-1)_{k-1}\big((x-1)^{n-k+1}+(-1)^{n-k}\big)$ proper $x$-colorings for all $x\geq k$. Engbers, Erey, Fox, and He proved this conjecture for $x=k$. In this paper, we prove Brown and Erey's conjecture under the condition that either the clique number of $G$ is $k$, or the independent number of $G$ is $2$.

math.CO↗

arXiv2023

Scale Filtering Analysis of Kinetic Reconnection and its Associated Turbulence

Previously, using an incompressible von Kármán-Howarth formalism, the behavior of cross-scale energy transfer in magnetic reconnection and turbulence was found to be essentially identical to each other, independent of an external magnetic (guide) field, in the inertial and energy-containing ranges (Adhikari et al., Phys. Plasmas 30, 082904, 2023). However, this description did not account for the energy transfer in the dissipation range for kinetic plasmas. In this letter, we adopt a scale-filtering approach to investigate this previously unaccounted-for energy transfer channel in reconnection. Using kinetic particle-in-cell (PIC) simulations of antiparallel and component reconnection, we show that the pressure-strain (PS) interaction becomes important at scales smaller than the ion inertial length, where the nonlinear energy transfer term drops off. Also, the presence of a guide field makes a significant difference in the morphology of the scale-filtered energy transfer. These results are consistent with kinetic turbulence simulations, suggesting that the pressure strain interaction is the dominant energy transfer channel between electron scales and ion scales.

physics.plasm-ph↗

arXiv2023

Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-training

In recent years, the growing demand for medical imaging diagnosis has placed a significant burden on radiologists. As a solution, Medical Vision-Language Pre-training (Med-VLP) methods have been proposed to learn universal representations from medical images and reports, benefiting downstream tasks without requiring fine-grained annotations. However, existing methods have overlooked the importance of cross-modal alignment in joint image-text reconstruction, resulting in insufficient cross-modal interaction. To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge. Furthermore, we introduce a Memory-Augmented Cross-Modal Fusion (MA-CMF) module to fully integrate visual information to assist report reconstruction and fuse the multi-modal representations adequately. Experimental results demonstrate that the proposed unified approach outperforms previous methods in all downstream tasks, including uni-modal, cross-modal, and multi-modal tasks.

cs.CV↗

arXiv2023

Effective Viscosity, Resistivity, and Reynolds Number in Weakly Collisional Plasma Turbulence

We examine dissipation and energy conversion in weakly collisional plasma turbulence, employing in situ observations from the Magnetospheric Multiscale (MMS) mission and kinetic Particle-in-Cell (PIC) simulations of proton-electron plasma. A previous result indicated the presence of viscous-like and resistive-like scaling of average energy conversion rates -- analogous to scalings characteristic of collisional systems. This allows for extraction of collisional-like coefficients of effective viscosity and resistivity, and thus also determination of effective Reynolds numbers based on these coefficients. The effective Reynolds number, as a measure of the available bandwidth for turbulence to populate various scales, links macro turbulence properties with kinetic plasma properties in a novel way.

physics.plasm-ph↗

arXiv2023

LCCo: Lending CLIP to Co-Segmentation

This paper studies co-segmenting the common semantic object in a set of images. Existing works either rely on carefully engineered networks to mine the implicit semantic information in visual features or require extra data (i.e., classification labels) for training. In this paper, we leverage the contrastive language-image pre-training framework (CLIP) for the task. With a backbone segmentation network that independently processes each image from the set, we introduce semantics from CLIP into the backbone features, refining them in a coarse-to-fine manner with three key modules: i) an image set feature correspondence module, encoding global consistent semantic information of the image set; ii) a CLIP interaction module, using CLIP-mined common semantics of the image set to refine the backbone feature; iii) a CLIP regularization module, drawing CLIP towards this co-segmentation task, identifying the best CLIP semantic and using it to regularize the backbone feature. Experiments on four standard co-segmentation benchmark datasets show that the performance of our method outperforms state-of-the-art methods.

cs.CV↗

arXiv2023

A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder

With the advent of the big data era, the data quality problem is becoming more critical. Among many factors, data with missing values is one primary issue, and thus developing effective imputation models is a key topic in the research community. Recently, a major research direction is to employ neural network models such as self-organizing mappings or automatic encoders for filling missing values. However, these classical methods can hardly discover interrelated features and common features simultaneously among data attributes. Especially, it is a very typical problem for classical autoencoders that they often learn invalid constant mappings, which dramatically hurts the filling performance. To solve the above-mentioned problems, we propose a missing-value-filling model based on a feature-fusion-enhanced autoencoder. We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features. Besides, we develop a missing value filling strategy based on dynamic clustering that is incorporated into an iterative optimization process. This design can enhance the multi-dimensional feature fusion ability and thus improves the dynamic collaborative missing-value-filling performance. The effectiveness of the proposed model is validated by extensive experiments compared to a variety of baseline methods on thirteen data sets.

cs.LG↗

arXiv2023

Coherent Dynamics of Charge Carriers in γ-InSe Revealed by Ultrafast Spectroscopy

For highly efficient ultrathin solar cells, layered indium selenide (InSe), a van der Waals solid, has shown a great promise. In this paper, we study the coherent dynamics of charge carriers generation in γ-InSe single crystals. We employ ultrafast transient absorption spectroscopy to examine the dynamics of hot electrons after resonant photoexcitation. To study the effect of excess kinetic energy of electrons after creating A exciton (VB1 to CB transition), we excite the sample with broadband pulses centered at 600, 650, 700 and 750 nm, respectively. We analyze the relaxation and recombination dynamics in γ-InSe by global fitting approach. Five decay associated spectra with their associated lifetimes are obtained, which have been assigned to intraband vibrational relaxation and interband recombination processes. We extract characteristic carrier thermalization times from 1 to 10 ps. To examine the coherent vibrations accompanying intraband relaxation dynamics, we analyze the kinetics by fitting to exponential functions and the obtained residuals are further processed for vibrational analysis. A few key phonon coherences are resolved and ab-initio quantum calculations reveal the nature of the associated phonons. The wavelet analysis is employed to study the time evolution of the observed coherences, which show that the low-frequency coherences last for more than 5 ps. Associated calculations reveal that the contribution of the intralayer phonon modes is the key determining factor for the scattering between free electrons and lattice. Our results provide fundamental insights into the photophysics in InSe and help to unravel their potential for high-performance optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2023

Event Camera Data Pre-training

This paper proposes a pre-trained neural network for handling event camera data. Our model is a self-supervised learning framework, and uses paired event camera data and natural RGB images for training. Our method contains three modules connected in a sequence: i) a family of event data augmentations, generating meaningful event images for self-supervised training; ii) a conditional masking strategy to sample informative event patches from event images, encouraging our model to capture the spatial layout of a scene and accelerating training; iii) a contrastive learning approach, enforcing the similarity of embeddings between matching event images, and between paired event and RGB images. An embedding projection loss is proposed to avoid the model collapse when enforcing the event image embedding similarities. A probability distribution alignment loss is proposed to encourage the event image to be consistent with its paired RGB image in the feature space. Transfer learning performance on downstream tasks shows the superiority of our method over state-of-the-art methods. For example, we achieve top-1 accuracy at 64.83% on the N-ImageNet dataset.

cs.CV↗

arXiv2023

Exploring the Potential of Integrated Optical Sensing and Communication (IOSAC) Systems with Si Waveguides for Future Networks

Advanced silicon photonic technologies enable integrated optical sensing and communication (IOSAC) in real time for the emerging application requirements of simultaneous sensing and communication for next-generation networks. Here, we propose and demonstrate the IOSAC system on the silicon nitride (SiN) photonics platform. The IOSAC devices based on microring resonators are capable of monitoring the variation of analytes, transmitting the information to the terminal along with the modulated optical signal in real-time, and replacing bulk optics in high-precision and high-speed applications. By directly integrating SiN ring resonators with optical communication networks, simultaneous sensing and optical communication are demonstrated by an optical signal transmission experimental system using especially filtering amplified spontaneous emission spectra. The refractive index (RI) sensing ring with a sensitivity of 172 nm/RIU, a figure of merit (FOM) of 1220, and a detection limit (DL) of 8.2*10-6 RIU is demonstrated. Simultaneously, the 1.25 Gbps optical on-off-keying (OOK) signal is transmitted at the concentration of different NaCl solutions, which indicates the bit-error-ratio (BER) decreases with the increase in concentration. The novel IOSAC technology shows the potential to realize high-performance simultaneous biosensing and communication in real time and further accelerate the development of IoT and 6G networks.

eess.SP↗

arXiv2023

Three-dimensional energy transfer in space plasma turbulence from multipoint measurement

A novel multispacecraft technique applied to Magnetospheric Multiscale (MMS) mission data collected in the Earth's magnetosheath enables evaluation of the energy cascade rate solving the full Yaglom's equation in a turbulent space plasma. The method differs from existing approaches in that (i) it is inherently three-dimensional; (ii) it provides a statistically significant number of estimates from a single data stream; and (iii) it allows for a direct visualization of energy flux in turbulent plasmas. This new technique will ultimately provide a realistic, comprehensive picture of the turbulence process in plasmas.

physics.space-ph↗

arXiv2023

Compressible Turbulence in the Near-Sun Solar Wind: Parker Solar Probe's First Eight Perihelia

Many questions remain about the compressibility of solar wind turbulence with respect to its origins and properties. Low plasma beta (ratio of thermal to magnetic pressure) environments allow for the easier generation of compressible turbulence, enabling study of the relationship between density fluctuations and turbulent Mach number. Utilizing Parker Solar Probe plasma data, we examine the normalized proton density fluctuations $\langle δn_p^2 \rangle ^{1/2}/\langle n_p\rangle = δ{n_p}_{rms}/\langle n_p\rangle$ as a function of turbulent Mach number $M_t$ conditioned on plasma beta and cross helicity. With consideration of statistical error in the parameters computed from in-situ data, we find a general result that $δ{n_p}_{rms}/\langle n_p\rangle \sim M_t^{1.18 \pm 0.04}$, consistent with both linear-wave theory, and nearly-incompressible turbulence in an inhomogeneous background field. We compare observational results conditioned on plasma beta and cross helicity with 3D magnetohydrodynamic simulations, and observe rather significant similarities with respect to how those parameters affect the proportionality between density fluctuations and turbulent Mach number. This study further investigates the complexity of compressible turbulence as viewed by the density scaling relationship, and may help better understand the compressible environment of the near-Sun solar wind.

astro-ph.SR↗

arXiv2023

Rapid Seismic Waveform Modeling and Inversion with Neural Operators

Seismic waveform modeling is a powerful tool for determining earth structure models and unraveling earthquake rupture processes, but it is usually computationally expensive. We introduce a scheme to vastly accelerate these calculations with a recently developed machine learning paradigm called the neural operator. Once trained, these models can simulate a full wavefield at negligible cost. We use a U-shaped neural operator to learn a general solution operator to the 2D elastic wave equation from an ensemble of numerical simulations performed with random velocity models and source locations. We show that full waveform modeling with neural operators is nearly two orders of magnitude faster than conventional numerical methods, and more importantly, the trained model enables accurate simulation for velocity models, source locations, and mesh discretization distinctly different from the training dataset. The method also enables convenient full-waveform inversion with automatic differentiation.

physics.geo-ph↗

arXiv2023

Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics.

physics.ins-det↗

arXiv2023

Relaxation of the turbulent magnetosheath

In turbulence, nonlinear terms drive energy transfer from large-scale eddies into small scales through the so-called energy cascade. Turbulence often relaxes toward states that minimize energy; typically these states are considered globally. However, turbulence can also relax toward local quasi-equilibrium states, creating patches or cells where the magnitude of nonlinearity is reduced and energy cascade is impaired. We show, for the first time, compelling observational evidence that this ``cellularization'' of turbulence can occur due to local relaxation in a strongly turbulent natural environment such as the Earth's magnetosheath.

physics.flu-dyn↗

arXiv2023

Multipoint Turbulence Analysis with Helioswarm

Exploration of plasma dynamics in space, including turbulence, is entering a new era of multi-satellite constellation measurements that will determine fundamental properties with unprecedented precision. Familiar but imprecise approximations will need to be abandoned and replaced with more advanced approaches. We present a preparatory study of the evaluation of second- and third-order statistics, using simultaneous measurements at many points. Here, for specificity, the orbital configuration of the NASA Helioswarm mission is employed in conjunction with three-dimensional magnetohydrodynamics numerical simulations of turbulence. The Helioswarm 9-spacecraft constellation flies virtually through the turbulence to compare results with the exact numerical statistics. We demonstrate novel increment-based techniques for the computation of (1) the multidimensional spectra and (2) the turbulent energy flux. This latter increment-space estimate of the cascade rate, based on the third-order Yaglom-Politano-Pouquet theory, uses numerous increment-space tetrahedra. Our investigation reveals that Helioswarm will provide crucial information on the nature of astrophysical turbulence.

physics.plasm-ph↗

arXiv2023

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%.

physics.ins-det↗

arXiv2022

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.

physics.ins-det↗

arXiv2022

Energy transfer and third-order law in forced anisotropic MHD turbulence with hyperviscosity

The Kolmogorov-Yaglom (third-order) law, links energy transfer rates in the inertial range of magneto-hydrodynamic (MHD) turbulence with third-order structure functions. Anisotropy, a typical property in the solar wind, largely challenges the applicability of the third-order law with isotropic assumption. To shed light on the energy transfer process in the presence of anisotropy, the present study conducted direct numerical simulations (DNSs) on forced MHD turbulence with normal and hyper-viscosity under various strengths of the external magnetic field ($B_0$), and calculated three forms of third-order structure function with or without averaging azimuthal or polar angles to $B_0$ direction. Correspondingly, three forms of estimated energy transfer rates were studied systematically with various $B_0$. The result shows that the peak of the estimated longitudinal transfer rate occurs at larger scales as closer to the $B_0$ direction, and its maximum shifts away from the $B_0$ direction at larger $B_0$. Compared with normal viscous cases, hyper-viscous cases can attain better separation of the inertial range from the dissipation range, thus facilitating the analyses of the inertial range properties and the estimation of the energy cascade rates. The direction-averaged third-order structure function over a spherical surface proposed in literature predicts the energy transfer rates and inertial range accurately, even at very high $B_0$. With limited statistics, the calculation of the third-order structure function shows a stronger dependence on averaging of azimuthal angles than the time, especially at high $B_0$ cases. These findings provide insights into the anisotropic effect on the estimation of energy transfer rates.

physics.flu-dyn↗