arXiv subjects

Jun He

Publications and source records attributed to Jun He.

At least 181 records · Page 10Linked to original sources

arXiv2022

Molecular states from $\bar{B}^{(*)}N$ interactions

In 2019, two new structures $Λ_b(6146)$ and $Λ_b(6152)$ were observed by the LHCb Collaboration at the invariant mass spectrum of $Λ_b^0π^{+}π^{-}$, which aroused a hot discussion about their inner structures. The $Λ_b(6146)$ and $Λ_b(6152)$ might still be molecular states because their masses are close to threshold of a $\bar{B}$ meson and a nucleon. In this work, we perform a systematical investigation of possible heavy baryonic molecular states from the $\bar{B}N$ interaction. Since the $\bar{B}N$ channel strongly couples to the $\bar{B}^{*}N$ channel, the possible $\bar{B}N-\bar{B}^{*}N$ bound states are also studied. The interaction of the system considered is described by the $t$-channel $σ$, $π$, $η$ ,$ω$, and $ρ$ mesons exchanges. By solving the non-relativistic Schrödinger equation with the obtained one-boson-exchange potentials, the $\bar{B}^{(*)}N$ bound states with different quantum numbers are searched. The calculation suggests that recently observed $Λ_b(6146)$ can be assigned as a $P$-wave $\bar{B}N$ molecular state with spin parity $J^P=3/2^{+}$ or a $\bar{B}N-\bar{B}^{*}N$ bound state. However, assignment of $Λ_b(6152)$ as an $F$-wave $\bar{B}N$ molecular is disfavored. The $Λ_b(6152)$ can be explained as meson-baryon molecular state with a small $\bar{B}N$ component. The calculation also predict the existence of two $S$-wave $\bar{B}N-\bar{B}^{*}N$ bound states that can be related to the experimentally observed $Λ_b(5912)$ and $Λ_b(5920)$.

hep-ph↗

arXiv2022

Deep Neural Network for Blind Visual Quality Assessment of 4K Content

The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement of spatial resolution. However, existing blind image quality assessment (BIQA) methods are not suitable for the original and upscaled 4K contents due to the expanded resolution and specific distortions. In this paper, we propose a deep learning-based BIQA model for 4K content, which on one hand can recognize true and pseudo 4K content and on the other hand can evaluate their perceptual visual quality. Considering the characteristic that high spatial resolution can represent more abundant high-frequency information, we first propose a Grey-level Co-occurrence Matrix (GLCM) based texture complexity measure to select three representative image patches from a 4K image, which can reduce the computational complexity and is proven to be very effective for the overall quality prediction through experiments. Then we extract different kinds of visual features from the intermediate layers of the convolutional neural network (CNN) and integrate them into the quality-aware feature representation. Finally, two multilayer perception (MLP) networks are utilized to map the quality-aware features into the class probability and the quality score for each patch respectively. The overall quality index is obtained through the average pooling of patch results. The proposed model is trained through the multi-task learning manner and we introduce an uncertainty principle to balance the losses of the classification and regression tasks. The experimental results show that the proposed model outperforms all compared BIQA metrics on four 4K content quality assessment databases.

cs.MM↗

arXiv2022

Systematical study of $Ω_c$-like molecular states from interactions $Ξ_c^{(',)}\bar{K}^{()}$ and $Ξ^{()}D^{()}$

In this work, the $Ω_c$-like molecular states are systematically investigated in a quasipotential Bethe-Salpeter equation approach. The relevant interactions $Ξ_c^{(*,')}\bar{K}^{(*)}$, $Ξ^{(*)}D^{(*)}$, and $Ω^{(*)}_c(π/η/ρ/ω)$ are described by light meson exchanges with the help of the effective Lagrangians with SU(3), chiral, and heavy quark symmetries. The obtained potential kernels of considered interactions are inserted into the quasipotential Bethe-Salpeter equation, and coupled-channel calculations are performed to find possible molecular states and its couplings to the channels considered. The results suggest that an isoscalar state can be produced from the $Ξ^*_c\bar{K}$ interaction with spin parity $3/2^-$, which can be related to state $Ω_c(3120)$. And its isoscalar partner is predicted with a dominant decay in the $Ω_c^*π$ channel. The isoscalar and isovector states with $1/2^-$ can be produced from the $Ξ'_c\bar{K}$ interaction with a threshold close to the mass of the $Ω_c(3050)$ and $Ω_c(3065)$. Their couplings to the $Ξ_c \bar{K}$ channel are very weak, and the isovector one has strong coupling to $Ω_cπ$. High precision measurement is helpful to confirm or search such molecular states. Experimental search of states with higher masses generated from interactions $Ξ^{(*,')}_c \bar{K}^*$ and $Ξ^* D^{(*)}$ are also suggested by the current results.

hep-ph↗

arXiv2022

Functional annotation of creeping bentgrass protein sequences based on convolutional neural network

Background: Creeping bentgrass (Agrostis soionifera) is a perennial grass of Gramineae, belonging to cold season turfgrass, but has poor disease resistance. Up to now, little is known about the induced systemic resistance (ISR) mechanism, especially the relevant functional proteins, which is important to disease resistance of turfgrass. Achieving more information of proteins of infected creeping bentgrass is helpful to understand the ISR mechanism. Results: With BDO treatment, creeping bentgrass seedlings were grown, and the ISR response was induced by infecting Rhizoctonia solani. High-quality protein sequences of creeping bentgrass seedlings were obtained. Some of protein sequences were functionally annotated according to the database alignment while a large part of the obtained protein sequences was left non-annotated. To treat the non-annotated sequences, a prediction model based on convolutional neural network was established with the dataset from Uniport database in three domains to acquire good performance, especially the higher false positive control rate. With established model, the non-annotated protein sequences of creeping bentgrass were analyzed to annotate proteins relevant to disease-resistance response and signal transduction. Conclusions: The prediction model based on convolutional neural network was successfully applied to select good candidates of the proteins with functions relevant to the ISR mechanism from the protein sequences which cannot be annotated by database alignment. The waste of sequence data can be avoided, and research time and labor will be saved in further research of protein of creeping bentgrass by molecular biology technology. It also provides reference for other sequence analysis of turfgrass disease-resistance research.

q-bio.GN↗

arXiv2022

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview

Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, survey study about the latest development of deep learning-based methods is lacking. Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this study is limited to methods taking monocular RGB/RGBD data as input and covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods of both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions.

cs.CV↗

arXiv2022

Angle-resolved photoemission spectroscopy study of the charge density wave order in layered semiconductor EuTe4

Layered tellurides have been extensively studied as a platform for investigating the Fermi surface (FS) nesting-driven charge density wave (CDW) states. EuTe4, one of quasi-two-dimensional (quasi-2D) binary rare-earth tetratellurides CDW compounds, with unconventional hysteretic transition, is currently receiving much attention. Here, the CDW modulation vector, momentum and temperature dependence of CDW gaps in EuTe4 are investigated using angle-resolved photoemission spectroscopy. Our results reveal that (i) a FS nesting vector q ~ 0.67 b* drives the formation of CDW state, (ii) a large anisotropic CDW gap is fully open in the whole FS, and maintains a considerable size even at 300 K, leading to appearance of semiconductor properties, (iii) an abnormal non-monotonic increase of CDW gap in magnitude as a function of temperature, (iv) an extra, larger gap opens at a higher binding energy due to the interaction between the different orbits of the main bands.

cond-mat.mtrl-sci↗

arXiv2022

Derivations, local and 2-local derivations of standard operator algebras

Let X be a Banach space over field F (R or C). Denote by B(X) the set of all bounded linear operators on X and by F(X) the set of all finite rank operators on X. A subalgebra A of B(X) is called a standard operator algebra if A contain F(X). We give a brief proof of a well-known result that every derivation from A into B(X) is inner. There is another classical result that every local derivation on B(X) is a derivation. We extend the result by proving that every local derivation from A into B(X) is a derivation. Based on these two results, we prove that every 2-local derivation from A into B(X) is a derivation.

math.FA↗

arXiv2022

Revisiting Local Descriptor for Improved Few-Shot Classification

Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images. In this context, recent research efforts have been aimed at designing more and more complex classifiers that measure similarities between query and support images, but left the importance of feature embeddings seldom explored. We show that the reliance on sophisticated classifiers is not necessary, and a simple classifier applied directly to improved feature embeddings can instead outperform most of the leading methods in the literature. To this end, we present a new method named \textbf{DCAP} for few-shot classification, in which we investigate how one can improve the quality of embeddings by leveraging \textbf{D}ense \textbf{C}lassification and \textbf{A}ttentive \textbf{P}ooling. Specifically, we propose to train a learner on base classes with abundant samples to solve dense classification problem first and then meta-train the learner on a bunch of randomly sampled few-shot tasks to adapt it to few-shot scenario or the test time scenario. During meta-training, we suggest to pool feature maps by applying attentive pooling instead of the widely used global average pooling (GAP) to prepare embeddings for few-shot classification. Attentive pooling learns to reweight local descriptors, explaining what the learner is looking for as evidence for decision making. Experiments on two benchmark datasets show the proposed method to be superior in multiple few-shot settings while being simpler and more explainable. Code is available at: \url{https://github.com/Ukeyboard/dcap/}.

cs.CV↗

arXiv2022

The quasi-fission phenomeonon of double charm $T_{cc}^+$ induced by nucleon

In this work, we study the reaction of a nucleon and a doubly charmed state $T_{cc}$. Under the assumption of the $T_{cc}$ as a molecular state of $D^{*}D$, the reaction of the nucleon and $T_{cc}$ is mediated by exchanges of $π$, $η$, $ρ$, and $ω$ meson, which results in split of $T_{cc}$ state with two $D$ mesons in final state. With the help of the effective Lagrangians, the cross section of $p+T^+_{cc}\to p+D^++D^0$ process is calculated, and a very large cross section can be obtained with very small incoming momentum of proton. It decrease rapidly with the increase of the momentum to about 10 mb at momenta of order of GeV. Such large cross section suggests that induced by a proton the $T_{cc}^+$ state is very easy to decay and transit to two $D$ mesons. In the rest frame of the $T_{cc}^+$ state, an obvious accumulation of final $D$ meson at small momentum region can be observed in predicted Dalitz plot, which is due to the molecular state interpretation of $T_{cc}$ state. This novel quasi-fission phenomenon of double charm molecular $T_{cc}^+$ state induced by a proton can be accessible at the forthcoming PANDA experiment.

hep-ph↗

arXiv2022

Ultrafast Optical Spectroscopy Evidence of Pseudogap and Electron-Phonon Coupling in an Iron-Based Superconductor KCa$_2$Fe$_4$As$_4$F$_2$

We use ultrafast optical spectroscopy to study the nonequilibrium quasiparticle relaxation dynamics of the iron-based superconductor KCa$_2$Fe$_4$As$_4$F$_2$ with $T_c=33.5$ K. Our results reveal a possible pseudogap ($Δ_{PG}$ = 2.4 $\pm$ 0.1 meV) below $T^*\approx 50$ K but prior to the opening of a superconducting gap ($Δ_{SC}$(0) $\approx$ 4.3 $\pm$ 0.1 meV). Measurements under high pump fluence real two distinct, coherent phonon oscillations with 1.95 and 5.51 THz frequencies, respectively. The high-frequency $A_{1g}$(2) mode corresponds to the $c-$axis polarized vibrations of FeAs planes with a nominal electron-phonon coupling constant $λ_{A_{1g}(2)}$ = 0.194 $\pm$ 0.02. Our findings suggest that the pseudogap is likely a precursor of superconductivity, and the electron-phonon coupling may play an essential role in the superconducting pairing in KCa$_2$Fe$_4$As$_4$F$_2$.

cond-mat.supr-con↗

arXiv2022

Incorporating Multiple Cluster Centers for Multi-Label Learning

Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Most of the existing approaches aim to improve the performance of multi-label learning by exploiting label correlations. Although the data augmentation technique is widely used in many machine learning tasks, it is still unclear whether data augmentation is helpful to multi-label learning. In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning. Specifically, we first propose a novel data augmentation approach that performs clustering on the real examples and treats the cluster centers as virtual examples, and these virtual examples naturally embody the local label correlations and label importances. Then, motivated by the cluster assumption that examples in the same cluster should have the same label, we propose a novel regularization term to bridge the gap between the real examples and virtual examples, which can promote the local smoothness of the learning function. Extensive experimental results on a number of real-world multi-label datasets clearly demonstrate that our proposed approach outperforms the state-of-the-art counterparts.

cs.LG↗

arXiv2021

BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing

Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data, achieving ground-breaking performance on various tasks such as node classification and graph property prediction. Nonetheless, existing systems are inefficient to train large graphs with billions of nodes and edges with GPUs. The main bottlenecks are the process of preparing data for GPUs - subgraph sampling and feature retrieving. This paper proposes BGL, a distributed GNN training system designed to address the bottlenecks with a few key ideas. First, we propose a dynamic cache engine to minimize feature retrieving traffic. By a co-design of caching policy and the order of sampling, we find a sweet spot of low overhead and high cache hit ratio. Second, we improve the graph partition algorithm to reduce cross-partition communication during subgraph sampling. Finally, careful resource isolation reduces contention between different data preprocessing stages. Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20.68x on average.

cs.LG↗

arXiv2021

SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition

Point cloud-based large scale place recognition is fundamental for many applications like Simultaneous Localization and Mapping (SLAM). Although many models have been proposed and have achieved good performance by learning short-range local features, long-range contextual properties have often been neglected. Moreover, the model size has also become a bottleneck for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net for large scale place recognition. Specifically, on top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed to learn both short-range local features and long-range contextual features in this model. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art on benchmark datasets in terms of both accuracy and speed with a super-light model size (0.9M). Meanwhile, two simplified versions of SVT-Net are introduced, which also achieve state-of-the-art and further reduce the model size to 0.8M and 0.4M respectively.

cs.CV↗

arXiv2021

Quantum-enhanced rubidium atomic magnetometer based on Faraday rotation via 795-nm Stokes operator squeezed light

With the help of Stokes operator S2 squeezed state (also called polarization squeezed state (PSS)) of 795-nm light, rubidium-87 (87Rb) atomic magnetometer based on Faraday rotation has been implemented and characterized.The PSS of Stokes operator S2 of 795-nm light has been prepared by means of coherently combining the polarization coherent state (PCS) of a linearly p-polarized bright 795-nm light beam and a linearly s-polarized squeezed vacuum state (SVS) generated by a 397.5-nm ultraviolet laser pumped sub-threshold optical parametric oscillator (OPO) with a PPKTP bulk crystal inside the OPO cavity.PSS with a squeezing level of -3.7 has been achieved around the analysis frequency of 10 kHz. At different transitions of D1 line, various frequency detuning, and reasonable atomic vapor cells temperature, Faraday rotation has been measured and compared.To decrease absorption (scattering) losses and the back-action from atomic spin noise to the probe beams polarization noise for maintaining the quantum properties of PSS of Stokes operator S2 of 795-nm light, we had to run our magnetometer with 87Rb vapor cells temperature below 60, at which the PSS was almost destroyed.The sensitivities of magnetic field measurement were characterized via measuring signal-to-noise ratio of the alternating current (AC) calibrated magnetic field signal with a balanced polarimeter. Under the conditions of the atomic number density of 5.8*1010 /cm3 and the probe beam with a detuning of - 400 MHz relative to the 5S1/2 (Fg=2) - 5P1/2 (Fe=1) transition of 87Rb D1 line, a typical sensitivity of 19.5 pT/Hz1/2 has been achieved employing PSS of Stokes operator S2 as the probe, compared with a sensitivity of 28.3 pT/Hz1/2 using PCS as the probe.We preliminarily demonstrated that the quantum-enhanced sensitivity in a Faraday-rotation-based 87Rb atomic magnetometer with the help of PSS of 795-nm light.

quant-ph↗

arXiv2021

Noise spectroscopy in Rydberg atomic ensemble

The phase noise of incident light fields can be converted into amplitude noise via absorption and dispersion effects in media under electromagnetically induced transparency. The conversion process is sensitive to two-photon detuning. This effect can be used to perform a novel type of Rydberg spectroscopy. We present experimental measurements of the phase noise spectrum (PNS) with Rydberg atoms in a cesium vapor cell. Furthermore, to explore microwave field measurements, a modulation-enhanced PNS approach is proposed.

physics.atom-ph↗

arXiv2021

ACR-Pose: Adversarial Canonical Representation Reconstruction Network for Category Level 6D Object Pose Estimation

Recently, category-level 6D object pose estimation has achieved significant improvements with the development of reconstructing canonical 3D representations. However, the reconstruction quality of existing methods is still far from excellent. In this paper, we propose a novel Adversarial Canonical Representation Reconstruction Network named ACR-Pose. ACR-Pose consists of a Reconstructor and a Discriminator. The Reconstructor is primarily composed of two novel sub-modules: Pose-Irrelevant Module (PIM) and Relational Reconstruction Module (RRM). PIM tends to learn canonical-related features to make the Reconstructor insensitive to rotation and translation, while RRM explores essential relational information between different input modalities to generate high-quality features. Subsequently, a Discriminator is employed to guide the Reconstructor to generate realistic canonical representations. The Reconstructor and the Discriminator learn to optimize through adversarial training. Experimental results on the prevalent NOCS-CAMERA and NOCS-REAL datasets demonstrate that our method achieves state-of-the-art performance.

cs.CV↗

arXiv2021

Enhancement of spin noise spectroscopy of rubidium atomic ensemble by using of the polarization squeezed light

We measured the spin noise spectroscopy (SNS) of rubidium atomic ensemble with two different atomic vapor cells (filled with the buffer gases or coated with paraffin film on the inner wall), and demonstrated the enhancement of signal to noise ratio (SNR) by using of the polarization squeezed state (PSS) of 795 nm light field with Stokes operator S2 squeezed. PSS is prepared by locking the relative phase between the squeezed vacuum state of light obtained by a sub-threshold optical parametric oscillator and the orthogonal polarized local oscillator beam by means of the quantum noise lock. Under the same conditions, PSS can be employed not only to improve SNR, but also to keep the full width at half maximum (FWHM) of SNS unchanged, compared with the case of using polarization coherent state (PCS), and the enhancement of SNR is positively correlated with the squeezing level of PSS. With the increase of probe laser power and atomic number density, the SNR and FWHM of SNS will increase correspondingly. With the help of PSS of Stokes operator S2, quantum enhancement of both SNR and FWHM of SNS signal has been demonstrated by controlling optical power of the S2 polarization squeezed light beam or atomic number density in our experiments.

quant-ph↗

arXiv2021

Nonlinear signal transduction network with multistate

Signal transduction is an important and basic mechanism to cell life activities. The stochastic state transition of receptor induces the release of signaling molecular, which triggers the state transition of other receptors. It constructs a nonlinear sigaling network, and leads to robust switchlike properties which are critical to biological function. Network architectures and state transitions of receptor affect the performance of this biological network. In this work, we perform a study of nonlinear signaling on biological polymorphic network by analyzing network dynamics of the Ca$^{2+}$ induced Ca$^{2+}$ release mechanism, where fast and slow processes are involved and the receptor has four conformational states. Three types of networks, Erdös-Rényi network, Watts-Strogatz network and BaraBási-Albert network, are considered with different parameters. The dynamics of the biological networks exhibit different patterns at different time scales. At short time scale, the second open state is essential to reproduce the quasi-bistable regime, which emerges at a critical strength of connection for all three states involved in the fast processes and disappears at another critical point. The pattern at short time scale is not sensitive to the network architecture. At long time scale, only monostable regime is observed, and difference of network architectures affects the results more seriously. Our finding identifies features of nonlinear signaling networks with multistate that may underlie their biological function.

q-bio.MN↗