arXiv subjects

Feng Yang

Publications and source records attributed to Feng Yang.

At least 127 records · Page 7Linked to original sources

arXiv2022

Joint Optimization of Preamble Selection and Access Barring for Random Access in MTC with General Device Activities

Most existing random access schemes for machine-type communications (MTC) simply adopt a uniform preamble selection distribution, irrespective of the underlying device activity distributions. Hence, they may yield unsatisfactory access efficiency. In this paper, we model device activities for MTC as multiple Bernoulli random variables following an arbitrary multivariate Bernoulli distribution which can reflect both dependent and independent device activities. Then, we optimize preamble selection and access barring for random access in MTC according to the underlying joint device activity distribution. Specifically, we investigate three cases of the joint device activity distribution, i.e., the cases of perfect, imperfect, and unknown joint device activity distributions, and formulate the average, worst-case average, and sample average throughput maximization problems, respectively. The problems in the three cases are challenging nonconvex problems. In the case of perfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain stationary points of the original problem and an approximate problem, respectively. In the case of imperfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of an equivalent problem and a stationary point of an approximate problem, respectively. Finally, in the case of unknown joint device activity distribution, we develop an iterative algorithm to obtain a stationary point. The proposed solutions are widely applicable and outperform existing solutions for dependent and independent device activities.

cs.IT↗

arXiv2022

Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification

Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision. Different from images where RGB pixels are stored in the regular grid, for point clouds, the underlying semantic and structural information of point clouds is the spatial layout of the points. Moreover, the properties of challenging in-context and background noise pose more challenges to point cloud analysis. One assumption is that the poor performance of the classification model can be attributed to the indistinguishable embedding feature that impedes the search for the optimal classifier. This work offers a new strategy for learning powerful representations via a contrastive learning approach that can be embedded into any point cloud classification network. First, we propose a supervised contrastive classification method to implement embedding feature distribution refinement by improving the intra-class compactness and inter-class separability. Second, to solve the confusion problem caused by small inter-class compactness and inter-class separability. Second, to solve the confusion problem caused by small inter-class variations between some similar-looking categories, we propose a confusion-prone class mining strategy to alleviate the confusion effect. Finally, considering that outliers of the sample clusters in the embedding space may cause performance degradation, we design an entropy-aware attention module with information entropy theory to identify the outlier cases and the unstable samples by measuring the uncertainty of predicted probability. The results of extensive experiments demonstrate that our method outperforms the state-of-the-art approaches by achieving 82.9% accuracy on the real-world ScanObjectNN dataset and substantial performance gains up to 2.9% in DCGNN, 3.1% in PointNet++, and 2.4% in GBNet.

cs.CV↗

arXiv2022

Ubiquitous Coexisting Electron-Mode Couplings in High Temperature Cuprate Superconductors

In conventional superconductors, the electron-phonon coupling plays a dominant role in pairing the electrons and generating superconductivity. In high temperature cuprate superconductors, the existence of the electron coupling with phonons and other boson modes and its role in producing high temperature superconductivity remain unclear. The evidence of the electron-boson coupling mainly comes from the angle-resolved photoemission (ARPES) observations of the ~70meV nodal dispersion kink and the ~40meV antinodal kink. However, the reported results are sporadic and the nature of the involved bosons are still under debate. Here we report new findings of ubiquitous two coexisting electron-mode couplings in cuprate superconductors. By taking ultra-high resolution laser-based ARPES measurements, combined with the improved second derivative analysis method, we discovered that the electrons are coupled simultaneously with two sharp phonon modes with energies of ~70meV and ~40meV in different superconductors with different doping levels, over the entire momentum space and at different temperatures above and below the superconducting transition temperature. The observed electron-phonon couplings are unusual because the associated energy scales do not exhibit an obvious change across the superconducting transition. We further find that the well-known "peak-dip-hump" structure, which has long been considered as a hallmark of superconductivity, is also omnipresent and consists of finer structures that originates from electron coupling with two sharp phonon modes. These comprehensive results provide a unified picture to reconcile all the reported observations and pinpoint the origin of the electron-mode couplings in cuprate superconductors. They provide key information to understand the role of the electron-phonon coupling in generating high temperature superconductivity.

cond-mat.supr-con↗

arXiv2021

Radiative decay of the $Ξ_c(2923)$ in a hadronic molecule picture

In the present work, we study the radiative decay of newly observed $Ξ_c(2923)^0$ based on the successful explanation that $Ξ_c(2923)^0$ is an $S$-wave $DΛ-DΣ$ molecular state in our previous study~\cite{Zhu:2020jke}. The radiative decay width of $DΛ-DΣ$ molecular state into $Ξ_c^0γ$ final state through hadronic loops are evaluated using effective Lagrangians. We find that decay width $Ξ_c(2923)^0\toΞ_c^0γ$ and $Ξ_c(2923)^0\toΞ_c^{'0}γ$ is evaluated to be approximately 1.23-11.66 KeV and 0.30-3.71 KeV, respectively. These are different from the results~\cite{Wang:2020gkn,Bijker:2020tns} that obtained by assuming $Ξ_c(2923)^0$ may be conventional charmed baryon. If measurements are in future experimental, these differences will be very useful to help us to test various interpretations of $Ξ_c(2923)^0$.

hep-ph↗

arXiv2021

Searching for strange hidden-charm pentaquark state $P_{cs}(4459)$ in $γp\to{}K^{+}P_{cs}(4459)$ reaction

We investigate the possibility of studying the strange hidden-charm pentaquark state $P_{cs}(4459)$ by photon-induced reactions on a proton target in an effective Lagrangian approach. The production process is described by the $t$-channel $K^{-}$ exchange, the $u$-channel $Λ$ exchange, the contract term, and the $s$- channel nucleon pole. Our theoretical approach is based on the assumption that $P_{cs}(4459)$ with $J^{P}=1/2^{-}$ or $J^{P}=3/2^{-}$ can be interpreted as a molecule composed of $\bar{D}^{*}Ξ_c$. Using the coupling constants of the $P^{J^P}_{cs}$ to $γΛ$ and $K^{-}p$ channels obtained from molecule picture of the $P^{J^{P}}_{cs}(4459)$, the total cross-sections of the process $γp\to{}P^{J^P}_{cs}K^{+}$ is evaluated. Our calculation indicates that the cross-section for $γp\to{}P^{1/2^{-}}_{cs}K^{+}$ and $γp\to{}P^{3/2^{-}}_{cs}K^{+}$ are of the order of 10.0 pb and 5.0 pb, respectively. In addition, we compute the cross-section by assuming $P_{cs}(4459)$ as a compact pentaquark and find it is quite different from the results of $\bar{D}^{*}Ξ_c$ molecule. Those results can be measured in future experiments, such as the Electron-Ion Collider in China and the United States. And can be used to test the nature of the $P_{cs}$.

hep-ph↗

arXiv2021

Strong decays of the $P_{cs}(4459)$ as a $Ξ_c\bar{D}^{*}$ molecule

In this work, we study the strong decay of the newly observed $P_{cs}(4459)$ assuming that it is a pure $Ξ_c\bar{D}^{*}$ molecular state. Considering two possible spin-parity assignments $J^P=1/2^{-}$ and $J^P=3/2^{-}$ the partial decay widths of the $Ξ_c\bar{D}^{*}$ molecular state into $J/ψΛ$, $D_s^{-}Λ_c^{+}$, and $DΞ_c^{(')}$ final states through hadronic loops are evaluated with the help of the effective Lagrangians. In comparison with the LHCb data, the spin-party $J^P=1/2^{-}$ the assignment is preferred while this of $J^P=3/2^{-}$ is disfavored. The $P_{cs}(4459)$ in spin-parity $J^P=3/2^{-}$ case maybe explained as $S$-wave coupled bound state with lager $Ξ_c\bar{D}^{*}$ component. In addition, the calculated partial decay widths with $J^P=1/2^{-}$ $Ξ_c\bar{D}^{*}$ molecular state picture indicates that allowed decay mode, $DΞ_c^{'}$, may have the biggest branching ratio. The experimental measurements for this strong decay process could be a crucial test for the molecule interpretation of the $P_{cs}(4459)$.

hep-ph↗

arXiv2021

COMISR: Compression-Informed Video Super-Resolution

Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression. However, most videos on the web or mobile devices are compressed, and the compression can be severe when the bandwidth is limited. In this paper, we propose a new compression-informed video super-resolution model to restore high-resolution content without introducing artifacts caused by compression. The proposed model consists of three modules for video super-resolution: bi-directional recurrent warping, detail-preserving flow estimation, and Laplacian enhancement. All these three modules are used to deal with compression properties such as the location of the intra-frames in the input and smoothness in the output frames. For thorough performance evaluation, we conducted extensive experiments on standard datasets with a wide range of compression rates, covering many real video use cases. We showed that our method not only recovers high-resolution content on uncompressed frames from the widely-used benchmark datasets, but also achieves state-of-the-art performance in super-resolving compressed videos based on numerous quantitative metrics. We also evaluated the proposed method by simulating streaming from YouTube to demonstrate its effectiveness and robustness. The source codes and trained models are available at https://github.com/google-research/google-research/tree/master/comisr.

cs.CV↗

arXiv2021

Extrinsic and Intrinsic Anomalous Metallic States in Transition Metal Dichalcogenide Ising Superconductors

The metallic ground state in two-dimensional (2D) superconductors has attracted much attention but is still under intense scrutiny. Especially, the measurements in ultralow temperature region are challenging for 2D superconductors due to the sensitivity to external perturbations. In this work, the resistance saturation induced by external noise, named as "extrinsic anomalous metallic state", is observed in 2D transition metal dichalcogenide (TMD) superconductor 4Ha-TaSe2 nanodevices. However, with further decreasing temperature, credible evidence of intrinsic anomalous metallic state is obtained by adequately filtering external radiation. Our work indicates that at ultralow temperatures the anomalous metallic state can be experimentally revealed as the quantum ground state in 2D crystalline TMD superconductors. Besides, Ising superconductivity revealed by ultrahigh in-plane critical field (Bc2//) going beyond the Pauli paramagnetic limit (Bp) is detected in 4Ha-TaSe2, from one-unit-cell device to bulk situation, which might be due to the weak coupling between the TaSe2 sub-monolayers.

cond-mat.supr-con↗

arXiv2021

MUSIQ: Multi-scale Image Quality Transformer

Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional neural networks (CNNs). The performance of CNN-based models is often compromised by the fixed shape constraint in batch training. To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation. To address this, we design a multi-scale image quality Transformer (MUSIQ) to process native resolution images with varying sizes and aspect ratios. With a multi-scale image representation, our proposed method can capture image quality at different granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale embedding is proposed to support the positional embedding in the multi-scale representation. Experimental results verify that our method can achieve state-of-the-art performance on multiple large scale IQA datasets such as PaQ-2-PiQ, SPAQ and KonIQ-10k.

cs.CV↗

arXiv2021

An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN

Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment. The reward function may be hard to model, and successful experience transitions are difficult to find in experience replay. In this context, this paper proposes an improved Double DQN (DDQN) to solve the problem by reference to A* and Rapidly-Exploring Random Tree (RRT). In order to achieve the rich experiments in experience replay, the initialization of robot in each training round is redefined based on RRT strategy. In addition, reward for the free positions is specially designed to accelerate the learning process according to the definition of position cost in A*. The simulation experimental results validate the efficiency of the improved DDQN, and robot could successfully learn the ability of obstacle avoidance and optimal path planning in which DQN or DDQN has no effect.

cs.RO↗

arXiv2021

Better Compression with Deep Pre-Editing

Could we compress images via standard codecs while avoiding visible artifacts? The answer is obvious -- this is doable as long as the bit budget is generous enough. What if the allocated bit-rate for compression is insufficient? Then unfortunately, artifacts are a fact of life. Many attempts were made over the years to fight this phenomenon, with various degrees of success. In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits. We design this editing operation as a learned convolutional neural network, and formulate an optimization problem for its training. Our loss takes into account a proximity between the original image and the edited one, a bit-budget penalty over the proposed image, and a no-reference image quality measure for forcing the outcome to be visually pleasing. The proposed approach is demonstrated on the popular JPEG compression, showing savings in bits and/or improvements in visual quality, obtained with intricate editing effects.

eess.IV↗

arXiv2021

Deep Perceptual Image Quality Assessment for Compression

Lossy Image compression is necessary for efficient storage and transfer of data. Typically the trade-off between bit-rate and quality determines the optimal compression level. This makes the image quality metric an integral part of any imaging system. While the existing full-reference metrics such as PSNR and SSIM may be less sensitive to perceptual quality, the recently introduced learning methods may fail to generalize to unseen data. In this paper we propose the largest image compression quality dataset to date with human perceptual preferences, enabling the use of deep learning, and we develop a full reference perceptual quality assessment metric for lossy image compression that outperforms the existing state-of-the-art methods. We show that the proposed model can effectively learn from thousands of examples available in the new dataset, and consequently it generalizes better to other unseen datasets of human perceptual preference.

cs.CV↗

arXiv2021

Adversarially Adaptive Normalization for Single Domain Generalization

Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversarial domain augmentation (ADA) to improve the model's generalization capability. The impact on domain generalization of the statistics of normalization layers is still underinvestigated. In this paper, we propose a generic normalization approach, adaptive standardization and rescaling normalization (ASR-Norm), to complement the missing part in previous works. ASR-Norm learns both the standardization and rescaling statistics via neural networks. This new form of normalization can be viewed as a generic form of the traditional normalizations. When trained with ADA, the statistics in ASR-Norm are learned to be adaptive to the data coming from different domains, and hence improves the model generalization performance across domains, especially on the target domain with large discrepancy from the source domain. The experimental results show that ASR-Norm can bring consistent improvement to the state-of-the-art ADA approaches by 1.6%, 2.7%, and 6.3% averagely on the Digits, CIFAR-10-C, and PACS benchmarks, respectively. As a generic tool, the improvement introduced by ASR-Norm is agnostic to the choice of ADA methods.

cs.CV↗

arXiv2021

DVMark: A Deep Multiscale Framework for Video Watermarking

Video watermarking embeds a message into a cover video in an imperceptible manner, which can be retrieved even if the video undergoes certain modifications or distortions. Traditional watermarking methods are often manually designed for particular types of distortions and thus cannot simultaneously handle a broad spectrum of distortions. To this end, we propose a robust deep learning-based solution for video watermarking that is end-to-end trainable. Our model consists of a novel multiscale design where the watermarks are distributed across multiple spatial-temporal scales. It gains robustness against various distortions through a differentiable distortion layer, whereas non-differentiable distortions, such as popular video compression standards, are modeled by a differentiable proxy. Extensive evaluations on a wide variety of distortions show that our method outperforms traditional video watermarking methods as well as deep image watermarking models by a large margin. We further demonstrate the practicality of our method on a realistic video-editing application.

cs.MM↗

arXiv2021

Genuine Electronic Structure and Superconducting Gap Structure in (Ba$_{0.6}$K$_{0.4}$)Fe$_{2}$As$_{2}$ Superconductor

The electronic structure and superconducting gap structure are prerequisites to establish microscopic theories in understanding the superconductivity mechanism of iron-based superconductors. However, even for the most extensively studied optimally-doped (Ba$_{0.6}$K$_{0.4}$)Fe$_2$As$_2$, there remain outstanding controversies on its electronic structure and superconducting gap structure. Here we resolve these issues by carrying out high-resolution angle-resolved photoemission spectroscopy (ARPES) measurements on the optimally-doped (Ba$_{0.6}$K$_{0.4}$)Fe$_2$As$_2$ superconductor using both Helium lamp and laser light sources. Our results indicate the "flat band" feature observed around the Brillouin zone center in the superconducting state originates from the combined effect of the superconductivity-induced band back-bending and the folding of a band from the zone corner to the center. We found direct evidence of the band folding between the zone corner and the center in both the normal and superconducting state. Our resolution of the origin of the flat band makes it possible to assign the three hole-like bands around the zone center and determine their superconducting gap correctly. Around the zone corner, we observe a tiny electron-like band and an M-shaped band simultaneously in both the normal and superconducting states. The obtained gap size for the bands around the zone corner ($\sim$5.5 meV) is significantly smaller than all the previous ARPES measurements. Our results establish a new superconducting gap structure around the zone corner and resolve a number of prominent controversies concerning the electronic structure and superconducting gap structure in the optimally-doped (Ba$_{0.6}$K$_{0.4}$)Fe$_2$As$_2$. They provide new insights in examining and establishing theories in understanding superconductivity mechanism in iron-based superconductors.

cond-mat.supr-con↗

arXiv2021

Multi-path Neural Networks for On-device Multi-domain Visual Classification

Learning multiple domains/tasks with a single model is important for improving data efficiency and lowering inference cost for numerous vision tasks, especially on resource-constrained mobile devices. However, hand-crafting a multi-domain/task model can be both tedious and challenging. This paper proposes a novel approach to automatically learn a multi-path network for multi-domain visual classification on mobile devices. The proposed multi-path network is learned from neural architecture search by applying one reinforcement learning controller for each domain to select the best path in the super-network created from a MobileNetV3-like search space. An adaptive balanced domain prioritization algorithm is proposed to balance optimizing the joint model on multiple domains simultaneously. The determined multi-path model selectively shares parameters across domains in shared nodes while keeping domain-specific parameters within non-shared nodes in individual domain paths. This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains. Extensive evaluations on the Visual Decathlon dataset demonstrate that the proposed multi-path model achieves state-of-the-art performance in terms of accuracy, model size, and FLOPS against other approaches using MobileNetV3-like architectures. Furthermore, the proposed method improves average accuracy over learning single-domain models individually, and reduces the total number of parameters and FLOPS by 78% and 32% respectively, compared to the approach that simply bundles single-domain models for multi-domain learning.

cs.CV↗

arXiv2021

Radiative decay of the $Ξ(1620)$ in a hadronic molecule picture

Last year, the $Ξ(1620)$ state that is cataloged in the Particle Data Group (PDG) with only one star is reported again in the $Ξ^{-}π^{+}$ final state by the Belle Collaboration. Its properties not only the spectroscopy but also the decay width cannot be simply explained in the context of conventional constituent quark models. This intrigues an active discussion on the structure of this resonance. In this work, we study the radiative decays of the newly observed $Ξ(1620)$ assuming that it is a meson-baryon molecular state of $Λ\bar{K}$ and $Σ\bar{K}$ with spin-parity $J^P=1/2^{-}$ in our previous work. The partial decay widths of the $Λ\bar{K}-Σ\bar{K}$ molecular state into $Ξγ$ and $Ξπγ$ final states through hadronic loops are evaluated with the help of the effective Lagrangians. The partial widths for the $Ξ(1620)^0\toγΞ$ and $Ξ(1620)^0\toγΞπ$ are evaluated to be about $118.76-174.21$ KeV and $58.19-68.75$ eV, respectively, which may be accessible for the LHCb. If the $Ξ(1620)$ is $Λ\bar{K}-Σ\bar{K}$ molecule, the radiative transition strength $Ξ(1620)^0\toγ\bar{K}Λ$ is quite small and the decay width is of the order of 0.01 eV. Future experimental measurements of these processes can be useful to test the molecule interpretations of the $Ξ(1620)$.

hep-ph↗

arXiv2021

Uniformity criterion for designs with both qualitative and quantitative factors

Experiments with both qualitative and quantitative factors occur frequently in practical applications. Many construction methods for this kind of designs, such as marginally coupled designs, were proposed to pursue some good space-filling structures. However, few criteria can be adapted to quantify the space-filling property of designs involving both qualitative and quantitative factors. As the uniformity is an important space-filling property of a design, in this paper, a new uniformity criterion, qualitative-quantitative discrepancy (QQD), is proposed for assessing the uniformity of designs with both types of factors. The closed form and lower bounds of the QQD are presented to calculate the exact QQD values of designs and recognize the uniform designs directly. In addition, a connection between the QQD and the balance pattern is derived, which not only helps to obtain a new lower bound but also provides a statistical justification of the QQD. Several examples show that the proposed criterion is reasonable and useful since it can distinguish distinct designs very well.

math.ST↗