arXiv subjects

Yi Zhang

Publications and source records attributed to Yi Zhang.

At least 937 records · Page 52Linked to original sources

arXiv2021

ASOD60K: An Audio-Induced Salient Object Detection Dataset for Panoramic Videos

Exploring to what humans pay attention in dynamic panoramic scenes is useful for many fundamental applications, including augmented reality (AR) in retail, AR-powered recruitment, and visual language navigation. With this goal in mind, we propose PV-SOD, a new task that aims to segment salient objects from panoramic videos. In contrast to existing fixation-/object-level saliency detection tasks, we focus on audio-induced salient object detection (SOD), where the salient objects are labeled with the guidance of audio-induced eye movements. To support this task, we collect the first large-scale dataset, named ASOD60K, which contains 4K-resolution video frames annotated with a six-level hierarchy, thus distinguishing itself with richness, diversity and quality. Specifically, each sequence is marked with both its super-/sub-class, with objects of each sub-class being further annotated with human eye fixations, bounding boxes, object-/instance-level masks, and associated attributes (e.g., geometrical distortion). These coarse-to-fine annotations enable detailed analysis for PV-SOD modelling, e.g., determining the major challenges for existing SOD models, and predicting scanpaths to study the long-term eye fixation behaviors of humans. We systematically benchmark 11 representative approaches on ASOD60K and derive several interesting findings. We hope this study could serve as a good starting point for advancing SOD research towards panoramic videos. The dataset and benchmark will be made publicly available at https://github.com/PanoAsh/ASOD60K.

cs.CV↗

arXiv2021

Discrete Quantum Geometry and Intrinsic Spin Hall Effect

We show that the quantum geometry of the Fermi surface can be numerically described by a 3-dimensional discrete quantum manifold. This approach not only avoids singularities in the Fermi sea, but it also enables the precise computation of the intrinsic Hall conductivity resolved in spin, as well as any other local properties of the Fermi surface. The method assures numerical accuracy when the Fermi level is arbitrarily close to singularities, and it remains robust when Kramers degeneracy is protected by symmetry. The approach is demonstrated by calculating the anomalous Hall and spin Hall conductivities of a 2-band lattice model of a Weyl semimetal and a full-band ab-initio model of zincblende GaAs.

cond-mat.mtrl-sci↗

arXiv2021

DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising

LDCT has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Various deep learning techniques have been introduced to improve the image quality of LDCT images through denoising. GANs-based denoising methods usually leverage an additional classification network, i.e. discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly; it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT images. Furthermore, the CutMix technique enables the per-pixel outputs of the U-Net based discriminator to provide radiologists with a confidence map to visualize the uncertainty of the denoised results, facilitating the LDCT-based screening and diagnosis. Extensive experiments on the simulated and real-world datasets demonstrate superior performance over recently published methods both qualitatively and quantitatively.

eess.IV↗

arXiv2021

MetroLoc: Metro Vehicle Mapping and Localization with LiDAR-Camera-Inertial Integration

We propose an accurate and robust multi-modal sensor fusion framework, MetroLoc, towards one of the most extreme scenarios, the large-scale metro vehicle localization and mapping. MetroLoc is built atop an IMU-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, and inertial information with the convenience of loosely coupled methods. The proposed framework is composed of three submodules: IMU odometry, LiDAR-inertial odometry (LIO), and Visual-inertial odometry (VIO). The IMU is treated as the primary sensor, which achieves the observations from LIO and VIO to constrain the accelerometer and gyroscope biases. Compared to previous point-only LIO methods, our approach leverages more geometry information by introducing both line and plane features into motion estimation. The VIO also utilizes the environmental structure information by employing both lines and points. Our proposed method has been extensively tested in the long-during metro environments with a maintenance vehicle. Experimental results show the system more accurate and robust than the state-of-the-art approaches with real-time performance. Besides, we develop a series of Virtual Reality (VR) applications towards efficient, economical, and interactive rail vehicle state and trackside infrastructure monitoring, which has already been deployed to an outdoor testing railroad.

cs.RO↗

arXiv2021

Emergent channel over a pair of pockets in strong density waves

Different channels over which electrons scatter between parts of the Fermi surface are the key to various electronic quantum matters, such as superconductivity and density waves. We consider an effective model in higher dimensions where each of the two pockets in the original model maps to (the Landau levels of) two Dirac fermions. We discover an emergent channel when two Dirac fermions from different pairs annihilate, where the presence of a strong density wave is essential. We support our analysis with numerical calculations on model examples in the vicinity of ferromagnetic and antiferromagnetic orders. We also discuss interesting consequences on electron interaction channels that beyond-mean-field fluctuations may induce.

cond-mat.str-el↗

arXiv2021

Unsupervised PET Reconstruction from a Bayesian Perspective

Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve imaging quality. Recently, the deep image prior (DIP) has drawn much attention and has been successfully applied in several image restoration tasks, such as denoising and inpainting, since it does not need any labels (reference image). However, overfitting is a vital defect of this framework. Hence, many methods have been proposed to mitigate this problem, and DeepRED is a typical representation that combines DIP and regularization by denoising (RED). In this article, we leverage DeepRED from a Bayesian perspective to reconstruct PET images from a single corrupted sinogram without any supervised or auxiliary information. In contrast to the conventional denoisers customarily used in RED, a DnCNN-like denoiser, which can add an adaptive constraint to DIP and facilitate the computation of derivation, is employed. Moreover, to further enhance the regularization, Gaussian noise is injected into the gradient updates, deriving a Markov chain Monte Carlo (MCMC) sampler. Experimental studies on brain and whole-body datasets demonstrate that our proposed method can achieve better performance in terms of qualitative and quantitative results compared to several classic and state-of-the-art methods.

eess.IV↗

arXiv2021

Learning Synergistic Attention for Light Field Salient Object Detection

We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms. Our SA-Net exploits the rich information of focal stacks via 3D convolutional neural networks, decodes the high-level features of multi-modal light field data with two cascaded synergistic attention modules, and predicts the saliency map using an effective feature fusion module in a progressive manner. Extensive experiments on three widely-used benchmark datasets show that our SA-Net outperforms 28 state-of-the-art models, sufficiently demonstrating its effectiveness and superiority. Our code is available at https://github.com/PanoAsh/SA-Net.

cs.CV↗

arXiv2021

A mass-, kinetic energy- and helicity-conserving mimetic dual-field discretization for three-dimensional incompressible Navier-Stokes equations, part I: Periodic domains

We introduce a mimetic dual-field discretization which conserves mass, kinetic energy and helicity for three-dimensional incompressible Navier-Stokes equations. The discretization makes use of a conservative dual-field mixed weak formulation where two evolution equations of velocity are employed and dual representations of the solution are sought for each variable. A temporal discretization, which staggers the evolution equations and handles the nonlinearity such that the resulting discrete algebraic systems are linear and decoupled, is constructed. The spatial discretization is mimetic in the sense that the finite dimensional function spaces form a discrete de Rham complex. Conservation of mass, kinetic energy and helicity in the absence of dissipative terms is proven at the discrete level. Proper dissipation rates of kinetic energy and helicity in the viscous case is also proven. Numerical tests supporting the method are provided.

math.NA↗

arXiv2021

Prospect of Detecting X-Ray Halos Around Middle-Aged Pulsars with eROSITA

The detection of extended TeV $γ$-ray emission (dubbed "TeV halos") from Geminga and Monogem pulsars by HAWC collaboration implies that the halo-like morphologies around middle-aged pulsars may be common. The $γ$-rays above 10 TeV are thought to arise from inverse Compton (IC) scattering of relativistic electrons/positrons in the pulsar halos off cosmic microwave background photons. In the meanwhile, these electrons and positrons can produce X-ray synchrotron emission in the interstellar magnetic field, resulting in a diffuse emission in the X-ray band (namely X-ray halos). Here, we study the prospect of detecting X-ray halos with eROSITA from 10 middle-aged pulsars with characteristic age larger than tens of thousands of years in the ATNF pulsar catalog. Assuming a benchmark value (i.e., $B = 3 \rm \, μG$) for the magnetic field, most of the X-ray halos are found to be bright enough to be detectable by eROSITA in the energy range of 0.5-2 keV during its four-year all-sky survey. Among these pulsar halos, three are supposed to produce X-ray fluxes above the eROSITA sensitivity of the first all-sky survey. Given the good angular resolution and the large field of view, eROSITA is expected to be able to measure the spatial distribution of the X-ray halos from sub-pc scale up to tens of pc scale. The intensity profiles of the X-ray halos are very useful to constrain the magnetic field and the energy-dependence of the diffusion coefficient in the pulsar halos.

astro-ph.HE↗

arXiv2021

Nuisance-Label Supervision: Robustness Improvement by Free Labels

In this paper, we present a Nuisance-label Supervision (NLS) module, which can make models more robust to nuisance factor variations. Nuisance factors are those irrelevant to a task, and an ideal model should be invariant to them. For example, an activity recognition model should perform consistently regardless of the change of clothes and background. But our experiments show existing models are far from this capability. So we explicitly supervise a model with nuisance labels to make extracted features less dependent on nuisance factors. Although the values of nuisance factors are rarely annotated, we demonstrate that besides existing annotations, nuisance labels can be acquired freely from data augmentation and synthetic data. Experiments show consistent improvement in robustness towards image corruption and appearance change in action recognition.

cs.CV↗

arXiv2021

Low-energy effective theory and symmetry classification of flux phases on Kagome lattice

Motivated by recent experiments on AV$_3$Sb$_5$ (A=K,Rb,Cs), the chiral flux phase has been proposed to explain time-reversal symmetry breaking. To fully understand the physics behind the chiral flux phase, we construct a low-energy effective theory based on the van-Hove points around the Fermi surface. The possible symmetry-breaking states and their classifications of the low-energy effective theory are completely studied, especially the flux phases on Kagome lattice. In addition, we discuss the relations between the low-energy symmetry breaking orders, the chiral flux and charge bond orders. We find all possible 183 flux phases on Kagome lattice within 2*2 unit cell by brute-force approach and classify them by point group symmetry. Among the 183 phases, we find 3 classes in 1*1 unit cell, 8 classes in 1*2 unit cell and 18 classes in 2*2 unit cell, respectively. These results provide a full picture of the time-reversal symmetry breaking in Kagome lattices.

cond-mat.str-el↗

arXiv2021

Prospects for the detection of the prompt very-high-energy emission from $\rmγ$-ray bursts with the High Altitude Detection of Astronomical Radiation experiment

The observation of very-high-energy (VHE, $\rm >10~GeV$) $γ$-ray emission from $\rm γ$-ray bursts (GRBs), especially in the prompt phase, will provide critical information for understanding many aspects of their nature including the physical environment, the relativistic bulk motion, the mechanisms of particle acceleration of GRBs and for studying Lorentz invariance violation, etc. For the afterglow phase, the highest energy photons detected to date by the imaging atmospheric Cherenkov telescopes extend to the TeV regime. However, for the prompt phase, years of efforts in searching for the VHE emission has yielded no statistically significant detections. A wide field-of-view (FOV) and large effective area above tens of GeV are essential for detecting the VHE emissions from GRBs in the prompt phase. The High Altitude Detection of Astronomical Radiation (HADAR) experiment has such merits. In this paper, we report the estimates of its expected annual GRB detection rate, which are obtained by combining the performance of the HADAR instrument with the theoretical calculations based on a phenomenological model to generate the pseudo-GRB population. The expected detectable gamma-ray signal from GRBs above the background is then obtained to give the detection rate. In the spectral model, an extra component is assigned to every GRB event in addition to the Band function. The results indicate that if the energy of the cutoff due to internal absorption is higher than 50 GeV, the detection rate for GRBs for the HADAR experiment is approximately two or three GRBs per year, which varies slightly depending upon the characteristics of the extra component.

astro-ph.HE↗

arXiv2021

An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design

As a promising tool to navigate in the vast chemical space, artificial intelligence (AI) is leveraged for drug design. From the year 2017 to 2021, the number of applications of several recent AI models (i.e. graph neural network (GNN), recurrent neural network (RNN), variation autoencoder (VAE), generative adversarial network (GAN), flow and reinforcement learning (RL)) in drug design increases significantly. Many relevant literature reviews exist. However, none of them provides an in-depth summary of many applications of the recent AI models in drug design. To complement the existing literature, this survey includes the theoretical development of the previously mentioned AI models and detailed summaries of 42 recent applications of AI in drug design. Concretely, 13 of them leverage GNN for molecular property prediction and 29 of them use RL and/or deep generative models for molecule generation and optimization. In most cases, the focus of the summary is the models, their variants, and modifications for specific tasks in drug design. Moreover, 60 additional applications of AI in molecule generation and optimization are briefly summarized in a table. Finally, this survey provides a holistic discussion of the abundant applications so that the tasks, potential solutions, and challenges in AI-based drug design become evident.

q-bio.QM↗

arXiv2021

Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings

Recent studies have proposed different methods to improve multilingual word representations in contextualized settings including techniques that align between source and target embedding spaces. For contextualized embeddings, alignment becomes more complex as we additionally take context into consideration. In this work, we propose using Optimal Transport (OT) as an alignment objective during fine-tuning to further improve multilingual contextualized representations for downstream cross-lingual transfer. This approach does not require word-alignment pairs prior to fine-tuning that may lead to sub-optimal matching and instead learns the word alignments within context in an unsupervised manner. It also allows different types of mappings due to soft matching between source and target sentences. We benchmark our proposed method on two tasks (XNLI and XQuAD) and achieve improvements over baselines as well as competitive results compared to similar recent works.

cs.CL↗

arXiv2021

Native point defects of semiconducting layered Bi2O2Se

Bi2O2Se is an emerging semiconducting, air-stable layered material (Nat. Nanotechnol. 2017, 12, 530; Nano Lett. 2017, 17, 3021), potentially exceeding MoS2 and phosphorene in electron mobility and rivalling typical Van der Waals stacked layered materials in the next-generation high-speed and low-power electronics. Holding the promise of functional versatility, it is arousing rapidly growing interest from various disciplines, including optoelectronics, thermoelectronics and piezoelectronics. In this work, we comprehensively study the electrical properties of the native point defects in Bi2O2Se, as an essential step toward understanding the fundamentals of this material. The defect landscapes dependent on both Fermi energy and the chemical potentials of atomic constituents are investigated. Along with the bulk defect analysis, a complementary inspection of the surface properties, within the simple context of charge neutrality level model, elucidates the observed n-type characteristics of Bi2O2Se based FETs. This work provides important guide to engineer the defects of Bi2O2Se for desired properties, which is key to the successful application of this emerging layered material.

cond-mat.mtrl-sci↗

arXiv2021

Potential PeVatron supernova remnant G106.3+2.7 seen in the highest-energy gamma rays

Cosmic rays (protons and other atomic nuclei) are believed to gain energies of petaelectronvolts (PeV) and beyond at astrophysical particle accelerators called 'PeVatrons' inside our Galaxy. Although a characteristic feature of a PeVatron is expected to be a hard gamma-ray energy spectrum that extends beyond 100 teraelectronvolts (TeV) without a cutoff, none of the currently known sources exhibits such a spectrum due to the low maximum energy of accelerated cosmic rays or insufficient detector sensitivity around 100 TeV. Here we report the observation of gamma-ray emission from the supernova remnant G106.3+2.7 above 10 TeV. This work provides flux data points up to and above 100 TeV and indicates that the very-high-energy gamma-ray emission above 10 TeV is well correlated with a molecular cloud rather than the pulsar PSR J2229+6114. Regarding the gamma-ray emission mechanism of G106.3+2.7, this morphological feature appears to favor a hadronic origin via the π0 decay caused by accelerated relativistic protons over a leptonic one via the inverse-Compton scattering by relativistic electrons. Furthermore, we point out that an X-ray flux upper limit on the synchrotron spectrum would provide important information to firmly establish the hadronic scenario as the mechanism of particle acceleration at the source.

astro-ph.HE↗

arXiv2021

Morphology of Gamma-Ray Halos around Middle-Aged Pulsars: Influence of the Pulsar Proper Motion

Recently, gamma-ray halos of a few degree extension have been detected around two middle-aged pulsars, namely, Geminga and PSR B0656+14, by the High Altitude Water Cherenkov observatory (HAWC). The gamma-ray radiation arise from relativistic electrons that escape the pulsar wind nebula and diffuse in the surrounding medium. The diffusion coefficient is found to be significantly lower than the average value in the Galactic disk. If so, given a typical transverse velocity of $300-500{\,\rm km /s}$ for a pulsar, its displacement could be important in shaping the morphology of its gamma-ray halos. Motivated by this, we study the morphology of pulsar halos considering the proper motion of pulsar. We define three evolutionary phases of pulsar halo to categorize its morphological features. The morphology of pulsar halos below 10$\,$TeV is double peaked or single peaked with an extended tail, which depends on the electron injection history. Above 10 TeV, the morphology of pulsar halos is nearly spherical, due to the short cooling timescale ($<50\,$kyr) for tens TeV electrons. We also quantitatively evaluate the separation between the pulsar and the center of the gamma-ray halo, as well as the influence of different assumptions for the pulsar characteristics and the injected electrons. Our results suggest that the separation between the center of the gamma-ray halo above 10$\,$TeV and the associated pulsar is usually too small to be observable by HAWC or LHAASO. Hence, our results provide a useful approach to constrain the origin of extended sources at very high energies.

astro-ph.HE↗

arXiv2021

Distinguish the $f(T)$ model from $Λ$CDM model with Gravitational Wave observations

Separately, neither electromagnetic (EM) observations nor gravitational wave (GW) observations can distinguish between the $f(T)$ model and the $Λ$CDM model effectively. To break this degeneration, we simulate the GW measurement based on the coming observation facilities, explicitly the Einstein Telescope. We make cross-validations between the simulated GW data and factual EM data, including the Pantheon, H(z), BAO and CMBR data, and the results show that they are consistent with each other. Anyway, the EM data itself have the $H_0$ tension problem which plays critical role in the distinguishable problem as we will see. Our results show that the GW$+$BAO$+$CMBR data could distinguish the $f(T)$ theory from the $Λ$CDM model in $2σ$ regime.

astro-ph.CO↗