arXiv subjects

Hui Li

Publications and source records attributed to Hui Li.

At least 757 records · Page 42Linked to original sources

arXiv2019

Energetic Particles of Cosmic Accelerators I: Galactic Accelerators

The high-energy universe has revealed that energetic particles are ubiquitous in the cosmos and play a vital role in the cultivation of cosmic environments on all scales. Energetic particles in our own galaxy, galactic cosmic rays (GCRs), engage in a complex interplay with the interstellar medium and magnetic fields in the galaxy, giving rise to many of its key characteristics. This White Paper is the first of a two-part series highlighting the most well-known high-energy cosmic accelerators and contributions that MeV gamma-ray astronomy will bring to understanding their energetic particle phenomena. The focus of this white paper is galactic cosmic rays, supernova remnants, protostellar jets and superbubbles, and colliding wind binaries.

astro-ph.HE↗

arXiv2019

Energetic Particles of Cosmic Accelerators II: Active Galactic Nuclei and Gamma-ray Bursts

The high-energy universe has revealed that energetic particles are ubiquitous in the cosmos and play a vital role in the cultivation of cosmic environments on all scales. Though they play a key role in cultivating the cosmological environment and/or enabling our studies of it, there is still much we do not know about AGNs and GRBs, particularly the avenue in which and through which they supply radiation and energetic particles, namely their jets. This White Paper is the second of a two-part series highlighting the most well-known high-energy cosmic accelerators and contributions that MeV gamma-ray astronomy will bring to understanding their energetic particle phenomena. The focus of this white paper is active galactic nuclei and gamma-ray bursts.

astro-ph.HE↗

arXiv2019

Probing the Emission Mechanism and Magnetic Field of Neutrino Blazars with Multi-Wavelength Polarization Signatures

The characteristic two-component blazar spectral energy distribution (SED) can be of either leptonic and/or hadronic origins. The potential association of the high-energy neutrino event IceCube-170922A with the flaring blazar TXS~0506+056 indicates that hadronic processes may operate in a blazar jet. Despite multi-wavelength follow-ups of the event and extensive theoretical modelings, the radiation mechanisms and the underlying magnetic field strength and configuration remain poorly understood. In this paper, we consider generic leptonic and hadronic blazar spectral models with distinct magnetic field strengths and radiation mechanisms. We analytically reproduce the SEDs and the neutrino flux of hadronic models, and predict their X-ray to $γ$-ray polarization degrees. Furthermore, by performing relativistic magnetohydrodynamic (RMHD) simulations taking into account the polarization-dependent radiation transfer, we study the time-dependent multi-wavelength polarization variability of the proton synchrotron model under a shock scenario. Our results suggest that the high-energy polarization degree and the neutrino flux can be jointly used to pinpoint the leptonic and/or hadronic blazar radiation mechanisms in the X-ray and $γ$-ray bands, and to infer the magnetic field strength in the emission region. Additionally, the temporal multi-wavelength polarization signatures in the proton synchrotron model shed light on the jet energy composition and the dynamical importance of magnetic fields in the blazar emission region. Future multi-wavelength polarimetry facilities such as {\it IXPE} and {\it AMEGO} together with neutrino telescopes such as {\it IceCube} can provide unprecedented observational constraints to probe the blazar radiation mechanisms and jet dynamics.

astro-ph.HE↗

arXiv2019

The star clusters that make black hole binaries across cosmic time

We explore the properties of dense star clusters that are likely to be the nurseries of stellar black holes pairing in close binaries. We combine a cosmological model of globular cluster formation with analytic prescriptions for the dynamical assembly and evolution of black hole binaries to constrain which types of clusters are most likely to form binaries tight enough to coalesce within a Hubble time. We find that black hole binaries which are ejected and later merge ex-situ form in clusters of a characteristic mass $M_{\rm cl} \sim 10^{5.3}M_{\odot}$, whereas binaries which merge in-situ form in more massive clusters, $M_{\rm cl} \sim 10^{5.7}M_{\odot}$. The clusters which dominate the production of black hole binaries are similar in age and metallicity to the entire population. Finally, we estimate an approximate cosmic black hole merger rate of dynamically assembled binaries using the mean black hole mass for each cluster given its metallicity. We find an intrinsic rate of $\sim 6\,\mathrm{Gpc\,yr^{-1}}$ at $z=0$, a weakly increasing merger rate out to $z=1.5$, and then a decrease out to $z=4$. Our results can be used to provide a cosmological context and choose initial conditions in numerical studies of black hole binaries assembled in star clusters.

astro-ph.GA↗

arXiv2019

Automatic trajectory measurement of large numbers of crowded objects

Complex motion patterns of natural systems, such as fish schools, bird flocks, and cell groups, have attracted great attention from scientists for years. Trajectory measurement of individuals is vital for quantitative and high-throughput study of their collective behaviors. However, such data are rare mainly due to the challenges of detection and tracking of large numbers of objects with similar visual features and frequent occlusions. We present an automatic and effective framework to measure trajectories of large numbers of crowded oval-shaped objects, such as fish and cells. We first use a novel dual ellipse locator to detect the coarse position of each individual and then propose a variance minimization active contour method to obtain the optimal segmentation results. For tracking, cost matrix of assignment between consecutive frames is trainable via a random forest classifier with many spatial, texture, and shape features. The optimal trajectories are found for the whole image sequence by solving two linear assignment problems. We evaluate the proposed method on many challenging data sets.

cs.CV↗

arXiv2019

DenseFuse: A Fusion Approach to Infrared and Visible Images

In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem. In contrast to conventional convolutional networks, our encoding network is combined by convolutional layers, fusion layer and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in encoding process. And two fusion layers(fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by decoder. Compared with existing fusion methods, the proposed fusion method achieves state-of-the-art performance in objective and subjective assessment. Code and pre-trained models are available at https://github.com/hli1221/imagefusion_densefuse

cs.CV↗

arXiv2018

The Crossover-Distance for ISI-Correcting Decoding of Convolutional Codes in Diffusion-Based Molecular Communications

In diffusion based molecular communication, the intersymbol interference (ISI) is an important reason for system performance degradation, which is caused by the random movement, out-of-order arrival and indistinguishability of the moleclues. In this paper, a new metric called crossover distance is introduced to measure the distance between the received bit sequence and the probably tranmitted bit sequences. A new decoding scheme of conventional codes is proposed based on crossover distance, which can enhance the communication reliability significantly. The theoretic analysis indicates that the proposed decoding algorithm provides an approximately maximal likelihood estimation of the information bits. The numerical results show that compared with uncoded systems and some existing channel codes, the proposed convolutional codes offer good performance with same throughputs.

cs.IT↗

arXiv2018

Double Neural Counterfactual Regret Minimization

Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG). However, the original CRF algorithm only works for discrete state and action spaces, and the resulting strategy is maintained as a tabular representation. Such tabular representation limits the method from being directly applied to large games and continuing to improve from a poor strategy profile. In this paper, we propose a double neural representation for the imperfect information games, where one neural network represents the cumulative regret, and the other represents the average strategy. Furthermore, we adopt the counterfactual regret minimization algorithm to optimize this double neural representation. To make neural learning efficient, we also developed several novel techniques including a robust sampling method, mini-batch Monte Carlo Counterfactual Regret Minimization (MCCFR) and Monte Carlo Counterfactual Regret Minimization Plus (MCCFR+) which may be of independent interests. Experimentally, we demonstrate that the proposed double neural algorithm converges significantly better than the reinforcement learning counterpart.

cs.AI↗

arXiv2018

Multi-focus Image Fusion using dictionary learning and Low-Rank Representation

Among the representation learning, the low-rank representation (LRR) is one of the hot research topics in many fields, especially in image processing and pattern recognition. Although LRR can capture the global structure, the ability of local structure preservation is limited because LRR lacks dictionary learning. In this paper, we propose a novel multi-focus image fusion method based on dictionary learning and LRR to get a better performance in both global and local structure. Firstly, the source images are divided into several patches by sliding window technique. Then, the patches are classified according to the Histogram of Oriented Gradient (HOG) features. And the sub-dictionaries of each class are learned by K-singular value decomposition (K-SVD) algorithm. Secondly, a global dictionary is constructed by combining these sub-dictionaries. Then, we use the global dictionary in LRR to obtain the LRR coefficients vector for each patch. Finally, the l_1-norm and choose-max fuse strategy for each coefficients vector is adopted to reconstruct fused image from the fused LRR coefficients and the global dictionary. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance in both qualitative and quantitative evaluations compared with serval classical methods and novel methods.The Code of our fusion method is available at https://github.com/hli1221/imagefusion_dllrr

cs.CV↗

arXiv2018

Hamiltonian circle actions with fixed point set almost minimal

Motivated by recent works on Hamiltonian circle actions satisfying certain minimal conditions, in this paper, we consider Hamiltonian circle actions satisfying an almost minimal condition. More precisely, we consider a compact symplectic manifold $(M, ω)$ admitting a Hamiltonian circle action with fixed point set consisting of two connected components $X$ and $Y$ satisfying $\dim(X)+\dim(Y)=\dim(M)$. Under certain cohomology conditions, we determine the circle action, the integral cohomology rings of $M$, $X$ and $Y$, and the total Chern classes of $M$, $X$, $Y$, and of the normal bundles of $X$ and $Y$. The results show that these data are unique --- they are exactly the same as those in the standard example $\Gt_2(\R^{2n+2})$, the Grassmannian of oriented $2$-planes in $\R^{2n+2}$, which is of dimension $4n$ with (any) $n\in\N$, equipped with a standard circle action. Moreover, if $M$ is Kähler and the action is holomorphic, we can use a few different criteria to claim that $M$ is $S^1$-equivariantly biholomorphic and $S^1$-equivariantly symplectomorphic to $\Gt_2(\R^{2n+2})$.

math.SG↗

arXiv2018

Infrared and Visible Image Fusion using a Deep Learning Framework

In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images. First, the source images are decomposed into base parts and detail content. Then the base parts are fused by weighted-averaging. For the detail content, we use a deep learning network to extract multi-layer features. Using these features, we use l_1-norm and weighted-average strategy to generate several candidates of the fused detail content. Once we get these candidates, the max selection strategy is used to get final fused detail content. Finally, the fused image will be reconstructed by combining the fused base part and detail content. The experimental results demonstrate that our proposed method achieves state-of-the-art performance in both objective assessment and visual quality. The Code of our fusion method is available at https://github.com/hli1221/imagefusion_deeplearning

cs.CV↗

arXiv2018

LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data

Data loss is a critical problem in structural health monitoring (SHM). Probability distributions play a highly important role in many applications. Improving the quality of distribution estimations made using incomplete samples is highly important. Missing samples can be compensated for by applying conventional missing data restoration methods; however, ensuring that restored samples roughly follow underlying distributions of true missing data remains a challenge. Another strategy involves directly restoring the probability density function (PDF) for a sensor when samples are missing by leveraging distribution information from another sensor with complete data using distribution regression techniques; existing methods include the conventional distribution-to-distribution regression (DDR) and distribution-to-warping function regression (DWR) methods. Due to constraints on PDFs and warping functions, the regression functions of both methods are estimated from the Nadaraya-Watson kernel estimator with relatively low degrees of precision. This article proposes a new indirect distribution-to-distribution regression method in the context of functional data analysis for restoring distributions of missing SHM data. PDFs are transformed to ordinary functions residing in a Hilbert space via the newly proposed log-quantile-density (LQD) transformation; the regression for distributions is realized in the transformed space via a functional regression model constructed based on the theory of Reproducing Kernel Hilbert Space (RKHS), corresponding result is subsequently mapped back to the density space through the inverse LQD transformation. Test results using field monitoring data indicate that the new method significantly outperforms conventional methods in general cases; however, in extrapolation cases, the new method is inferior to the distribution-to-warping function regression method.

stat.AP↗

arXiv2018

Gamma-ray Production in the Extended Halo of the Galaxy and Possible Implications for the Origin of Galactic Cosmic Rays

Various studies have implied the existence of a gaseous halo around the Galaxy extending out to 100 kpc. Galactic cosmic rays (CRs) that propagate to the halo, either by diffusion or by convection with the possibly existing large-scale Galactic wind, can interact with the gas therein and produce gamma-rays via proton-proton collision. We calculate the cosmic ray distribution in the halo and the gamma-ray flux, and explore the dependence of the result on model parameters such as diffusion coefficient, CR luminosity, CR spectral index. We find that the current measurement of isotropic gamma-ray background at $\lesssim$TeV with Fermi Large Area Telescope already approaches a level that can provide interesting constraints on the properties of Galactic cosmic ray (e.g., with CR luminosity $L_{CR}\leq 10^{41}$erg/s). We also discuss the possibilities of the Fermi bubble and IceCube neutrinos originating from the proton-proton collision between cosmic rays and gas in the halo, as well as the implication of our results for the baryon budget of the hot circumgalactic medium of our Galaxy. Given that the isotropic gamma-ray background is likely to be dominated by unresolved extragalactic sources, future telescopes may extract more individual sources from the IGRB, and hence put even more stringent restriction on the relevant quantities (such as Galactic cosmic ray luminosity and baryon budget in the halo) in the presence of a turbulent halo that we consider.

astro-ph.HE↗

arXiv2018

Visual Question Answering as Reading Comprehension

Visual question answering (VQA) demands simultaneous comprehension of both the image visual content and natural language questions. In some cases, the reasoning needs the help of common sense or general knowledge which usually appear in the form of text. Current methods jointly embed both the visual information and the textual feature into the same space. However, how to model the complex interactions between the two different modalities is not an easy task. In contrast to struggling on multimodal feature fusion, in this paper, we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem. With this transformation, our method not only can tackle VQA datasets that focus on observation based questions, but can also be naturally extended to handle knowledge-based VQA which requires to explore large-scale external knowledge base. It is a step towards being able to exploit large volumes of text and natural language processing techniques to address VQA problem. Two types of models are proposed to deal with open-ended VQA and multiple-choice VQA respectively. We evaluate our models on three VQA benchmarks. The comparable performance with the state-of-the-art demonstrates the effectiveness of the proposed method.

cs.CV↗

arXiv2018

Automatically Generate Steganographic Text Based on Markov Model and Huffman Coding

Steganography, as one of the three basic information security systems, has long played an important role in safeguarding the privacy and confidentiality of data in cyberspace. The text is the most widely used information carrier in people's daily life, using text as a carrier for information hiding has broad research prospects. However, due to the high coding degree and less information redundancy in the text, it has been an extremely challenging problem to hide information in it for a long time. In this paper, we propose a steganography method which can automatically generate steganographic text based on the Markov chain model and Huffman coding. It can automatically generate fluent text carrier in terms of secret information which need to be embedded. The proposed model can learn from a large number of samples written by people and obtain a good estimate of the statistical language model. We evaluated the proposed model from several perspectives. Experimental results show that the performance of the proposed model is superior to all the previous related methods in terms of information imperceptibility and information hidden capacity.

cs.CR↗

arXiv2018

On the error analyses of polarization measurements of the white-light coronagraph aboard ASO-S

The Advanced Space-based Solar Observatory (ASO-S) mission aims to explore two most spectacular eruptions in the Sun: solar flares and coronal mass ejections (CMEs), and their magnetism. For the studies of CMEs, the payload Lyman-alpha Solar Telescope (LST) has been proposed. It includes a traditional white-light coronagraph and a Lyman-alpha coronagraph which opens a new window to CME observations. Polarization measurements taken by white-light coronagraphs are crucial to derive fundamental physical parameters of CMEs. To make such measurements, there are two options of Stokes polarimeter which have been used by existing white-light coronagraphs for space missions. One uses a single or triple linear polarizers, the other involves both a half-wave plate and a linear polarizer. We find that the former option subjects to less uncertainty in the derived Stokes vector propagated from detector noise. The latter option involves two plates which are prone to internal reflections and may have a reduced transmission factor. Therefore, the former option is adopted as our Stokes polarimeter scheme for LST. Based on the parameters of the intended linear polarizer(s) colorPol provided by CODIXX and the half-wave plate 2-APW-L2-012C by Altechna, it is further shown that the imperfect maximum transmittance of the polarizer significantly increases the variance amplification of Stokes vector by at least about 50% when compared with the ideal case. The relative errors of Stokes vector caused by the imperfection of colorPol polarizer and the uncertainty due to the polarizer assembling in the telescope are estimated to be about 5%. Among the considered parameters, we find that the dominant error comes from the uncertainty in the maximum transmittance of the polarizer.

astro-ph.IM↗

arXiv2018

Science with a ngVLA: Imaging planetary systems in the act of forming with the ngVLA

The discovery of thousands of exoplanets has shown that the birth of planets is a very efficient process in nature. Several physical mechanisms have been proposed to describe the assembly of planets in disks surrounding young stars. However, observational constraints have been sparse on account of insufficient sensitivity and resolution, especially for imaging the inner $10 - 20$ au from the star, where most of planets are observed. Thanks to its unprecedented angular resolution and sensitivity at wavelengths where the emission from the circumstellar material is optically thin, the ngVLA has the potential to transform our understanding of planet formation. In this chapter, we present state-of-the-art theoretical models of planetary systems in the act of forming that demonstrate ngVLA capabilities of imaging and follow the temporal evolution of young solar system analogues up to the distance of the Orion Nebula. These images will unveil how planets form and interact with their parental disks, and will shed light on the diverse properties of exoplanetary systems.

astro-ph.EP↗

arXiv2018

Large-scale Compression Acceleration during Magnetic Reconnection in a Low-$β$ Plasma

In solar flares and other astrophysical systems, a major challenge for solving particle acceleration problem associated with magnetic reconnection is the enormous scale separation between kinetic scales and observed reconnection scale. Because of this, it has been difficult to draw any definite conclusions by just using kinetic simulations. Particle acceleration model that solves energetic particle transport equation can capture the main acceleration physics found in kinetic simulations, and thus provide a practical way to make observable predictions and directly compare model results with observations. Here we study compression particle acceleration in magnetic reconnection by solving Parker (diffusion-advection) transport equation using velocity and magnetic fields from two-dimensional high-Lundquist-number magnetohydrodynamics (MHD) simulations of a low-$β$ reconnection layer. We show that the compressible reconnection layer can give significant particle acceleration, leading to the formation of power-law particle energy distributions. We analyze the acceleration rate and find that the acceleration in the reconnection layer is a mixture of first order and second order Fermi processes. When including a guide field, we find the spectrum becomes steeper and both the power-law cutoff energy and maximum particle energy decrease as plasma becomes less compressible. This model produces 2D particle distribution that one can use to generate radiation map and directly compare with solar flare observations. This provides a framework to explain particle acceleration at large-scale astrophysical reconnection sites, such as solar flares.

astro-ph.SR↗