arXiv subjects

Ping Zhou

Publications and source records attributed to Ping Zhou.

At least 91 records · Page 5Linked to original sources

arXiv2022

Dependence of Chemical Abundance on the Cosmic Ray Ionization Rate in IC 348

Ions (e.g., H$_3^+$, H$_2$O$^+$) have been used extensively to quantify the cosmic-ray ionization rate (CRIR) in diffuse sightlines. However, measurements of CRIR in low-to-intermediate density gas environments are rare, especially when background stars are absent. In this work, we combine molecular line observations of CO, OH, CH, and HCO$^+$ in the star-forming cloud IC~348, and chemical models to constrain the value of CRIR and study the response of the chemical abundances distribution. The cloud boundary is found to have an $A_{\rm V}$ of approximately 4 mag. From the interior to the exterior of the cloud, the observed $^{13}$CO line intensities drop by an order of magnitude. The calculated average abundance of $^{12}$CO (assuming $^{12}$C/$^{13}$C = 65) is (1.2$\pm$0.9) $\times$10$^{-4}$, which increases by a factor of 6 from the interior to the outside regions. The average abundance of CH (3.3$\pm$0.7 $\times$ 10$^{-8}$) is in good agreement with previous findings in diffuse and translucent clouds ($A_{\rm V}$ $<$ 5 mag). However, we did not find a decline in CH abundance in regions of high extinction ($A_{\rm V}\simeq$8 mag) as previously reported in Taurus. By comparing the observed molecular abundances and chemical models, we find a decreasing trend of CRIR as $A_{\rm V}$ increases. The inferred CRIR of $ζ_{cr}$ = (4.7$\pm$1.5) $\times$ 10$^{-16}$ s$^{-1}$ at low $A_{\rm V}$ is consistent with H$^+_3$ measurements toward two nearby massive stars.

astro-ph.GA↗

arXiv2022

Ultrafast transport and energy relaxation of hot electrons in Au/Fe/MgO(001) heterostructures analyzed by linear time-resolved photoelectron spectroscopy

In condensed matter, scattering processes determine the transport of charge carriers. In case of heterostructures, interfaces determine many dynamic properties like charge transfer and transport and spin current dynamics. Here we discuss optically excited electron dynamics and their propagation across a lattice-matched, metal-metal interface of single crystal quality. Using femtosecond time-resolved linear photoelectron spectroscopy upon optically pumping different constituents of the heterostructure we establish a technique which probes the electron propagation and its energy relaxation simultaneously. In our approach a near-infrared pump pulse excites electrons directly either in the Au layer or in the Fe layer of epitaxial Au/Fe/MgO(001) heterostructures while the transient photoemission spectrum is measured by an ultraviolet probe pulse on the Au surface. Upon femtosecond laser excitation, we analyze the relative changes in the electron distribution close to the Fermi energy and assign characteristic features of the time-dependent electron distribution to transport of hot and non-thermalized electrons from the Fe layer to the Au surface and vice versa. From the measured transient electron distribution we determine the excess energy which we compare with a calculation based on the two-temperature model and takes diffusive electron transport into account. On this basis we identify a transition from a super-diffusive to a diffusive transport regime to occur for a Au layer thickness of 20-30~nm.

cond-mat.mtrl-sci↗

arXiv2022

X-ray polarization detection of Cassiopeia A with IXPE

We report on a $\sim 5σ$ detection of polarized 3-6 keV X-ray emission from the supernova remnant Cassiopeia A with the Imaging X-ray Polarimetry Explorer (IXPE). The overall polarization degree of $1.8 \pm 0.3$% is detected by summing over a large region, assuming circular symmetry for the polarization vectors. The measurements imply an average polarization degree for the synchrotron component of $\sim 2.5$%, and close to 5% for the X-ray synchrotron-domimated forward-shock region. These numbers are based on an assessment of the thermal and non-thermal radiation contributions, for which we used a detailed spatial-spectral model based on Chandra X-ray data. A pixel-by-pixel search for polarization provides a few tentative detections from discrete regions at the $\sim 3σ$ confidence level. Given the number of pixels, the significance is {insufficient} to claim a detection for individual pixels, but implies considerable turbulence on scales smaller than the angular resolution. Cas A's X-ray continuum emission is dominated by synchrotron radiation from regions within $\lesssim 10^{17}$ cm of the forward- and reverse shocks. We find that i) the measured polarization angle corresponds to a radially-oriented magnetic field, similar to what has been inferred from radio observations; ii) the X-ray polarization degree is lower than in the radio band ($\sim 5$%). Since shock compression should impose a tangential magnetic field structure, the IXPE results imply that magnetic-fields are reoriented within $\sim 10^{17}$cm of the shock. If the magnetic-field alignment is due to locally enhanced acceleration near quasi-parallel shocks, the preferred X-ray polarization angle suggests a size of $3\times 10^{16}$ cm for cells with radial magnetic fields.

astro-ph.HE↗

arXiv2022

FFCNet: Fourier Transform-Based Frequency Learning and Complex Convolutional Network for Colon Disease Classification

Reliable automatic classification of colonoscopy images is of great significance in assessing the stage of colonic lesions and formulating appropriate treatment plans. However, it is challenging due to uneven brightness, location variability, inter-class similarity, and intra-class dissimilarity, affecting the classification accuracy. To address the above issues, we propose a Fourier-based Frequency Complex Network (FFCNet) for colon disease classification in this study. Specifically, FFCNet is a novel complex network that enables the combination of complex convolutional networks with frequency learning to overcome the loss of phase information caused by real convolution operations. Also, our Fourier transform transfers the average brightness of an image to a point in the spectrum (the DC component), alleviating the effects of uneven brightness by decoupling image content and brightness. Moreover, the image patch scrambling module in FFCNet generates random local spectral blocks, empowering the network to learn long-range and local diseasespecific features and improving the discriminative ability of hard samples. We evaluated the proposed FFCNet on an in-house dataset with 2568 colonoscopy images, showing our method achieves high performance outperforming previous state-of-the art methods with an accuracy of 86:35% and an accuracy of 4.46% higher than the backbone. The project page with code is available at https://github.com/soleilssss/FFCNet.

eess.IV↗

arXiv2022

3D Face Parsing via Surface Parameterization and 2D Semantic Segmentation Network

Face parsing assigns pixel-wise semantic labels as the face representation for computers, which is the fundamental part of many advanced face technologies. Compared with 2D face parsing, 3D face parsing shows more potential to achieve better performance and further application, but it is still challenging due to 3D mesh data computation. Recent works introduced different methods for 3D surface segmentation, while the performance is still limited. In this paper, we propose a method based on the "3D-2D-3D" strategy to accomplish 3D face parsing. The topological disk-like 2D face image containing spatial and textural information is transformed from the sampled 3D face data through the face parameterization algorithm, and a specific 2D network called CPFNet is proposed to achieve the semantic segmentation of the 2D parameterized face data with multi-scale technologies and feature aggregation. The 2D semantic result is then inversely re-mapped to 3D face data, which finally achieves the 3D face parsing. Experimental results show that both CPFNet and the "3D-2D-3D" strategy accomplish high-quality 3D face parsing and outperform state-of-the-art 2D networks as well as 3D methods in both qualitative and quantitative comparisons.

cs.CV↗

arXiv2022

Unusually high HCO+/CO ratios in and outside supernova remnant W49B

Galactic supernova remnants (SNRs) and their environments provide the nearest laboratories to study SN feedback. We performed molecular observations toward SNR W49B, the most luminous Galactic SNR in the X-ray band, aiming to explore signs of multiple feedback channels of SNRs on nearby molecular clouds (MCs). We found very broad HCO+ lines with widths of dv = 48--75 km/s in the SNR southwest, providing strong evidence that W49B is perturbing MCs at a systemic velocity of $V_{LSR}=61$--65 km/s, and placing W49B at a distance of $7.9\pm 0.6$ kpc. We observed unusually high-intensity ratios of HCO+ J=1-0/CO J=1-0 not only at shocked regions ($1.1\pm 0.4$ and $0.70\pm 0.16$), but also in quiescent clouds over 1 pc away from the SNR's eastern boundary (> 0.2). By comparing with the magnetohydrodynamics shock models, we interpret that the high ratio in the broad-line regions can result from a cosmic-ray (CR) induced chemistry in shocked MCs, where the CR ionization rate is enhanced to around 10--100 times of the Galactic level. The high HCO+/CO ratio outside the SNR is probably caused by the radiation precursor, while the luminous X-ray emission of W49B can explain a few properties in this region. The above results provide observational evidence that SNRs can strongly influence the molecular chemistry in and outside the shock boundary via their shocks, CRs, and radiation. We propose that the HCO+/CO ratio is a potentially useful tool to probe an SNR's multichannel influence on MCs.

astro-ph.GA↗

arXiv2022

GeV Gamma-ray Emission and Molecular Clouds towards Supernova Remnant G35.6$-$0.4 and the TeV Source HESS J1858+020

It is difficult to distinguish hadronic process from the leptonic one in $γ$-ray observation, which is however crucial in revealing the origin of cosmic rays. As an endeavor in the regard, we focus in this work on the complex $γ$-ray emitting region, which partially overlaps with the unidentified TeV source HESS~J1858+020 and includes supernova remnant (SNR) G35.6$-$0.4 and HII region G35.6$-$0.5. We reanalyze CO-line, HI, and Fermi-LAT GeV $γ$-ray emission data of this region. The analysis of the molecular and HI data suggests that SNR G35.6$-$0.4 and HII region G35.6$-$0.5 are located at different distances. The analysis the GeV $γ$-rays shows that GeV emission arises from two point sources: one (SrcA) coincident with the SNR, and the other (SrcB) coincident with both HESS J1858+020 and HII region G35.6$-$0.5. The GeV emission of SrcA can be explained by the hadronic process in the SNR-MC association scenario. The GeV-band spectrum of SrcB and the TeV-band spectrum of HESS J1858+020 can be smoothly connected by a power-law function, with an index of $\sim$2.2. The connected spectrum is well explained with a hadronic emission, with the cutoff energy of protons above 1 PeV. It thus indicates that there is a potential PeVatron in the HII region and should be further verified with ultra-high energy observations with, e.g., LHAASO.

astro-ph.HE↗

arXiv2022

Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. In this work, we provide a two-pronged approach to this challenge: (i) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs. Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.

cs.DC↗

arXiv2022

Proximity-Induced Superconductivity in Atomically Precise Nanographene

Obtaining a robust superconducting state in atomically precise nanographene (NG) structures by proximity to a superconductor could foster the discovery of topological superconductivity in graphene. On-surface synthesis of such NGs has been achieved on noble metals or metal oxides, however, it is still absent on superconductors. Here, we present a synthetic method to induce superconductivity to polymeric chains and NGs adsorbed on the superconducting Nb(110) substrate covered by thin Ag films. Using atomic force microscopy at low-temperature, we characterize the chemical structure of each sub-product formed on the superconducting Ag layer. Scanning tunneling spectroscopy further allows us to elucidate electronic properties of these nanostructures, which consistently show a superconducting gap. We foresee our approach to become a promising platform for exploring the interplay between carbon magnetism and superconductivity at the fundamental level.

cond-mat.supr-con↗

arXiv2022

Pulmonary Fissure Segmentation in CT Images Based on ODoS Filter and Shape Features

Priori knowledge of pulmonary anatomy plays a vital role in diagnosis of lung diseases. In CT images, pulmonary fissure segmentation is a formidable mission due to various of factors. To address the challenge, an useful approach based on ODoS filter and shape features is presented for pulmonary fissure segmentation. Here, we adopt an ODoS filter by merging the orientation information and magnitude information to highlight structure features for fissure enhancement, which can effectively distinguish between pulmonary fissures and clutters. Motivated by the fact that pulmonary fissures appear as linear structures in 2D space and planar structures in 3D space in orientation field, an orientation curvature criterion and an orientation partition scheme are fused to separate fissure patches and other structures in different orientation partition, which can suppress parts of clutters. Considering the shape difference between pulmonary fissures and tubular structures in magnitude field, a shape measure approach and a 3D skeletonization model are combined to segment pulmonary fissures for clutters removal. When applying our scheme to 55 chest CT scans which acquired from a publicly available LOLA11 datasets, the median F1-score, False Discovery Rate (FDR), and False Negative Rate (FNR) respectively are 0.896, 0.109, and 0.100, which indicates that the presented method has a satisfactory pulmonary fissure segmentation performance.

eess.IV↗

arXiv2022

AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images

Multi-sequence cardiac magnetic resonance (CMR) provides essential pathology information (scar and edema) to diagnose myocardial infarction. However, automatic pathology segmentation can be challenging due to the difficulty of effectively exploring the underlying information from the multi-sequence CMR data. This paper aims to tackle the scar and edema segmentation from multi-sequence CMR with a novel auto-weighted supervision framework, where the interactions among different supervised layers are explored under a task-specific objective using reinforcement learning. Furthermore, we design a coarse-to-fine framework to boost the small myocardial pathology region segmentation with shape prior knowledge. The coarse segmentation model identifies the left ventricle myocardial structure as a shape prior, while the fine segmentation model integrates a pixel-wise attention strategy with an auto-weighted supervision model to learn and extract salient pathological structures from the multi-sequence CMR data. Extensive experimental results on a publicly available dataset from Myocardial pathology segmentation combining multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising performance compared with other state-of-the-art methods. Our method is promising in advancing the myocardial pathology assessment on multi-sequence CMR data. To motivate the community, we have made our code publicly available via https://github.com/soleilssss/AWSnet/tree/master.

eess.IV↗

arXiv2021

IRAM 30 m CO-line Observation toward PeVatron Candidate G106.3+2.7: Direct Interaction between the Shock and the Molecular Cloud Remains Uncertain

Supernova remnant (SNR) G106.3+2.7 was recently found to be one of the few potential Galactic hadronic PeVatrons. Aiming to test how solid the SNR is associated with the molecular clouds (MCs) that are thought to be responsible for hadronic interaction, we performed a new CO observation with the IRAM 30m telescope toward its "belly" region, which is coincident with the centroid of the $γ$-ray emission. There is a filament structure in the local-standard-of-rest velocity interval $-8$ to $-5$ km/s that nicely follows the northern radio boundary of the SNR. We have seen asymmetric broad profiles of $^{12}$CO lines, with widths of a few km/s along the northern boundary and in the "belly" region of G106.3+2.7, but similar $^{12}$CO line profiles are also found outside the SNR boundary. Further, the low $^{12}$CO J=2-1/J=1-0 line ratios suggest the MCs are cool. Therefore, it is still uncertain whether the MCs are directly disturbed by the SNR shocks, but we do find some clues that the MCs are nearby and thus can still be illuminated by the escaped protons from the SNR. Notably, we find an expanding molecular structure with a velocity of $\sim$3.5 km/s and a velocity gradient of the MCs across the SNR from $\sim -3$ to $-7$ km/s, which could be explained as the effect of the wind blown by the SNR's progenitor star.

astro-ph.GA↗

arXiv2021

Spatially Resolved X-ray Study of Supernova Remnant G306.3$-$0.9 with Unusually High Calcium Abundance

G306.3$-$0.9 is an asymmetric Galactic supernova remnant (SNR), whose progenitor has been thought to be a Type Ia supernova (SN), but its high Ca abundance appears inconsistent with the Type Ia origin. Hoping to uncover the reason for its asymmetry and the origin of this SNR, we performed a spatially resolved X-ray spectroscopic analysis of XMM-Newton and Chandra observation data. We divided the SNR into 13 regions and analyzed the spectra using two-temperature models (0.2 keV + 1 keV). Compared to the southwestern regions, the northeastern regions have higher metal abundances and a lower gas density. This suggests that the asymmetric morphology results from the non-uniform ambient environment. We found that neither Type Ia nor core-collapse SN models can account for the abnormally high abundance ratios of Ar/Si, Ca/Si, or the shape of the abundance curve. A comparison with the Ca-rich transient models shows that G306.3$-$0.9 is likely to be the first identified Galactic "Ca-rich transient" remnant, although the theoretical production of element S is lower. We also note that the conclusion for the SNR's origin relies on the measured abundance ratios and existing nucleosynthesis models. Between two groups of Ca-rich transient explosion models, we prefer the He shell detonation for an accreting WD, rather than the merger of a white dwarf and a neutron star.

astro-ph.HE↗

arXiv2021

Elemental Abundances of the Hot Atmosphere of Luminous Infrared Galaxy Arp 299

Hot atmospheres of massive galaxies are enriched with metals. Elemental abundances measured in the X-ray band have been used to study the chemical enrichment of supernova remnants, elliptical galaxies, groups and clusters of galaxies. Here we measure the elemental abundances of the hot atmosphere of luminous infrared galaxy Arp 299 observed with XMM-Newton. To measure the abundances in the hot atmosphere, we use a multi-temperature thermal plasma model, which provides a better fit to the Reflection Grating Spectrometer data. The observed Fe/O abundance ratio is subsolar, while those of Ne/O and Mg/O are slightly above solar. Core-collapse supernovae (SNcc) are the dominant metal factory of elements like O, Ne, and Mg. We find some deviations between the observed abundance patterns and theoretical ones from a simple chemical enrichment model. One possible explanation is that massive stars with $M_{\star}\gtrsim23-27~M_{\odot}$ might not explode as SNcc and enrich the hot atmosphere. This is in accordance with the missing massive SNcc progenitors problem, where very massive progenitors $M_{\star}\gtrsim18~M_{\odot}$ of SNcc have not been clearly detected. It is also possible that theoretical SNcc nucleosynthesis yields of Mg/O yields are underestimated.

astro-ph.HE↗

arXiv2021

The Post-impact Evolution of the X-ray Emitting Gas in SNR 1987A Viewed by XMM-Newton

Since 1996 the blast wave driven by SN 1987A has been interacting with the dense circumstellar material, which provides us with a unique opportunity to study the early evolution of a newborn supernova remnant (SNR). Based on the XMM-Newton RGS and EPIC-pn X-ray observations from 2007 to 2019, we investigated the post-impact evolution of the X-ray emitting gas in SNR 1987A. The hot plasma is represented by two non-equilibrium ionization components with temperature of $\sim0.6$ keV and $\sim2.5$ keV. The low-temperature plasma has a density $\sim2400$ cm$^{-3}$, which is likely dominated by the lower density gas inside the equatorial ring (ER). The high-temperature plasma with a density $\sim550$ cm$^{-3}$ could be dominated by the H II region and the high-latitude material beyond the ring. In the last few years, the emission measure of the low-temperature plasma has been decreasing, indicating that the blast wave has left the main ER. But the blast wave is still propagating into the high-latitude gas, resulting in the steadily increase of the high-temperature emission measure. In the meantime, the average abundances of N, O, Ne, and Mg are found to be declining, which may reflect the different chemical compositions between two plasma components. We also detected the Fe K lines in most of the observations, showing increasing flux and centroid energy. We interpret the Fe K lines as from a third hot component, which may come from the reflected shock-heated gas or originate from Fe-rich ejecta clumps, shocked by the reverse shock.

astro-ph.HE↗

arXiv2020

Chemical abundances in Sgr A East: evidence for a Type Iax supernova remnant

Recent observations have shown a remarkable diversity of observational behaviors and explosion mechanisms in thermonuclear supernovae (SNe). An emerging class of peculiar thermonuclear SNe, called Type Iax, show photometric and spectroscopic behaviors distinct from normal Type Ia. Their origin remains highly controversial, but pure turbulent deflagration of white dwarfs (WDs) has been regarded as the leading formation theory. The large population of Type Iax indicates the existence of unidentified Galactic Type Iax supernova remnants (SNRs). We report evidence that SNR Sgr A East in the Galactic center resulted from a pure turbulent deflagration of a Chandrasekhar-mass carbon-oxygen WD, an explosion mechanism used for Type Iax SNe. Our X-ray spectroscopic study of Sgr A East using 3 Ms of Chandra data shows a low ratio of intermediate-mass elements to Fe and large Mn/Fe and Ni/Fe ratios. This abundance pattern does not accord with the core-collapse or normal Type Ia models. Sgr A East is thus the first Galactic SNR for which a likely Type Iax origin has been proposed and the nearest target for studying this peculiar class. We compared Sgr A East with the Fe-rich SNRs 3C 397 and W49B, which also have high Mn and Cr abundances and were claimed to result from deflagration-to-detonation explosions of Chandrasekhar-mass WDs (although with disputes). Our study shows that they have distinct abundance patterns. The X-ray spectroscopic studies of thermonuclear SNRs provide observational evidence for the theories that there are diverse explosion channels and various metal outputs for Chandrasekhar-mass WDs.

astro-ph.HE↗

arXiv2020

Revisiting the distance, environment and supernova properties of SNR G57.2+0.8 that hosts SGR 1935+2154

We have performed a multi-wavelength study of supernova remnant (SNR) G57.2+0.8 and its environment. The SNR hosts the magnetar SGR 1935+2154, which emitted an extremely bright ms-duration radio burst on 2020 Apr 28 (The Chime/Frb Collaboration et al. 2020; Bochenek et al. 2020). We used the 12CO and 13CO J=1-0 data from the Milky Way Image Scroll Painting (MWISP) CO line survey to search for molecular gas associated with G57.2+0.8, in order to constrain the physical parameters (e.g., the distance) of the SNR and its magnetar. We report that SNR G57.2+0.8 is likely impacting the molecular clouds (MCs) at the local standard of rest (LSR) velocity V_{LSR} ~ 30 km/s and excites a weak 1720 MHz OH maser with a peak flux density of 47 mJy/beam. The chance coincidence of a random OH spot falling in the SNR is <12%, and the OH-CO correspondence chance is 7% at the maser spot. This combines to give < 1% false probability of the OH maser, suggesting a real maser detection. The LSR velocity of the MCs places the SNR and magnetar at a kinematic distance of d=6.6 +/- 0.7 kpc. The nondetection of thermal X-ray emission from the SNR and the relatively dense environment suggests G57.2+0.8 be an evolved SNR with an age $t>1.6 \times 10^4$ (d/6.6 kpc) yr. The explosion energy of G57.2+0.8 is lower than $2 \times 10^{51}(n_0/10 cm^{-3})^{1.16} (d/~6.6 kpc)^{3.16}$ erg, which is not very energetic even assuming a high ambient density $n_0$ = 10 cm$^{-3}$. This reinforces the opinion that magnetars do not necessarily result from very energetic supernova explosions.

astro-ph.HE↗

arXiv2020

Communication-Efficient Distributed Estimator for Generalized Linear Models with a Diverging Number of Covariates

Distributed statistical inference has recently attracted immense attention. The asymptotic efficiency of the maximum likelihood estimator (MLE), the one-step MLE, and the aggregated estimating equation estimator are established for generalized linear models under the "large $n$, diverging $p_n$" framework, where the dimension of the covariates $p_n$ grows to infinity at a polynomial rate $o(n^α)$ for some $0<α<1$. Then a novel method is proposed to obtain an asymptotically efficient estimator for large-scale distributed data by two rounds of communication. In this novel method, the assumption on the number of servers is more relaxed and thus practical for real-world applications. Simulations and a case study demonstrate the satisfactory finite-sample performance of the proposed estimators.

stat.ME↗