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Guang Yang

Publications and source records attributed to Guang Yang.

At least 469 records · Page 26Linked to original sources
arXiv2020

JWST/MIRI Simulated Imaging: Insights into Obscured Star-Formation and AGN for Distant Galaxies in Deep Surveys

The JWST MIRI instrument will revolutionize extragalactic astronomy with unprecedented sensitivity and angular resolution in mid-IR. Here, we assess the potential of MIRI photometry to constrain galaxy properties in the Cosmic Evolution Early Release Science (CEERS) survey. We derive estimated MIRI fluxes from the spectral energy distributions (SEDs) of real sources that fall in a planned MIRI pointing. We also obtain MIRI fluxes for hypothetical AGN-galaxy mixed models varying the AGN fractional contribution to the total IR luminosity ($\rm frac_{AGN}$). Based on these model fluxes, we simulate CEERS imaging (3.6-hour exposure) in 6 bands from F770W to F2100W using MIRISIM, and reduce these data using JWST PIPELINE. We perform PSF-matched photometry with TPHOT, and fit the source SEDs with X-CIGALE, simultaneously modeling photometric redshift and other physical properties. Adding the MIRI data, the accuracy of both redshift and $\rm frac_{AGN}$ is generally improved by factors of $\gtrsim 2$ for all sources at $z\lesssim 3$. Notably, for pure-galaxy inputs ($\rm frac_{AGN}=0$), the accuracy of $\rm frac_{AGN}$ is improved by $\sim 100$ times thanks to MIRI. The simulated CEERS MIRI data are slightly more sensitive to AGN detections than the deepest X-ray survey, based on the empirical $L_{\rm X}$-$L_{\rm 6μm}$ relation. Like X-ray observations, MIRI can also be used to constrain the AGN accretion power (accuracy $\approx 0.3$ dex). Our work demonstrates that MIRI will be able to place strong constraints on the mid-IR luminosities from star formation and AGN, and thereby facilitate studies of the galaxy/AGN co-evolution.

astro-ph.GA↗
arXiv2020

Piercing through Highly Obscured and Compton-thick AGNs in the Chandra Deep Fields. II. Are Highly Obscured AGNs the Missing Link in the Merger-Triggered AGN-Galaxy Coevolution Models?

By using a large highly obscured ($N_{\rm H} > 10^{23}\ \rm cm^{-2}$) AGN sample (294 sources at $z \sim 0-5$) selected from detailed X-ray spectral analyses in the deepest Chandra surveys, we explore distributions of these X-ray sources in various optical/IR/X-ray color-color diagrams and their host-galaxy properties, aiming at characterizing the nuclear obscuration environment and the triggering mechanism of highly obscured AGNs. We find that the refined IRAC color-color diagram fails to identify the majority of X-ray selected highly obscured AGNs, even for the most luminous sources with ${\rm log}\,L_{\rm X}\, \rm (erg\ s^{-1}) > 44$. Over 80% of our sources will not be selected as heavily obscured candidates using the flux ratio of $f_{\rm 24 μm}\, /\,f_R > 1000$ and $R - K > 4.5$ criteria, implying complex origins and conditions for the obscuring materials that are responsible for the heavy X-ray obscuration. The average star formation rate of highly obscured AGNs is similar to that of stellar mass- ($M_*$-) and $z$-controlled normal galaxies, while the lack of quiescent hosts is observed for the former. Partial correlation analyses imply that highly obscured AGN activity (traced by $L_{\rm X}$) appears to be more fundamentally related to $M_*$, and no dependence of $N_{\rm H}$ on either $M_*$ or SFR is detected. Morphology analyses reveal that 61% of our sources have a significant disk component, while only 27% of them exhibit irregular morphological signatures. These findings together point toward a scenario where secular processes (e.g., galactic-disk instabilities), instead of mergers, are most probable to be the leading mechanism that triggers accretion activities of X-ray-selected highly obscured AGNs.

astro-ph.GA↗
arXiv2020

Direct Quantification for Coronary Artery Stenosis Using Multiview Learning

The quantification of the coronary artery stenosis is of significant clinical importance in coronary artery disease diagnosis and intervention treatment. It aims to quantify the morphological indices of the coronary artery lesions such as minimum lumen diameter, reference vessel diameter, lesion length, and these indices are the reference of the interventional stent placement. In this study, we propose a direct multiview quantitative coronary angiography (DMQCA) model as an automatic clinical tool to quantify the coronary artery stenosis from X-ray coronary angiography images. The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module, and a regression module, to achieve direct accurate multiple-index estimation. The multi-view module comprehensively learns the Spatio-temporal features of coronary arteries through a three-dimensional convolution. The attention mechanisms of each view focus on the subtle feature of the lesion region and capture the important context information. The key-frame module learns the subtle features of the stenosis through successive dilated residual blocks. The regression module finally generates the indices estimation from multiple features.

eess.IV↗
arXiv2020

Annealing Genetic GAN for Minority Oversampling

The key to overcome class imbalance problems is to capture the distribution of minority class accurately. Generative Adversarial Networks (GANs) have shown some potentials to tackle class imbalance problems due to their capability of reproducing data distributions given ample training data samples. However, the scarce samples of one or more classes still pose a great challenge for GANs to learn accurate distributions for the minority classes. In this work, we propose an Annealing Genetic GAN (AGGAN) method, which aims to reproduce the distributions closest to the ones of the minority classes using only limited data samples. Our AGGAN renovates the training of GANs as an evolutionary process that incorporates the mechanism of simulated annealing. In particular, the generator uses different training strategies to generate multiple offspring and retain the best. Then, we use the Metropolis criterion in the simulated annealing to decide whether we should update the best offspring for the generator. As the Metropolis criterion allows a certain chance to accept the worse solutions, it enables our AGGAN steering away from the local optimum. According to both theoretical analysis and experimental studies on multiple imbalanced image datasets, we prove that the proposed training strategy can enable our AGGAN to reproduce the distributions of minority classes from scarce samples and provide an effective and robust solution for the class imbalance problem.

cs.LG↗
arXiv2020

Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness

The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts. Although many deep learning-based CS-MRI methods have been proposed to mitigate the problems of traditional methods, they have not been able to achieve more robust results at higher acceleration factors. Most of the deep learning-based CS-MRI methods still can not fully mine the information from the k-space, which leads to unsatisfactory results in the MRI reconstruction. In this study, we propose a new deep learning-based CS-MRI reconstruction method to fully utilise the relationship among sequential MRI slices by coupling Wasserstein Generative Adversarial Networks (WGAN) with Recurrent Neural Networks. Further development of an attentive unit enables our model to reconstruct more accurate anatomical structures for the MRI data. By experimenting on different MRI datasets, we have demonstrated that our method can not only achieve better results compared to the state-of-the-arts but can also effectively reduce residual noise generated during the reconstruction process.

eess.IV↗
arXiv2020

Non-uniform Sampled Motion Planning for Continuous-time STL

This paper presents an offline motion planner for linear cyber-physical systems that satisfy a continuous-time Signal Temporal Logic (STL) specification, in which controls are applied in a Zeroth-order Hold (ZOH) manner. The motion planning problem is formulated as a Mixed-integer Program (MIP) with nonuniform control updates. We develop a novel method to obtain bounds of Control Barrier Functions (CBF) and linear predicates to render both spatial and temporal requirements. The theoretical results are validated in numerical examples.

eess.SY↗
arXiv2020

GHAST: Breaking Confirmation Delay Barrier in Nakamoto Consensus via Adaptive Weighted Blocks

Initiated from Nakamoto's Bitcoin system, blockchain technology has demonstrated great capability of building secure consensus among decentralized parties at Internet-scale, i.e., without relying on any centralized trusted party. Nowadays, blockchain systems find applications in various fields. But the performance is increasingly becoming a bottleneck, especially when permissionless participation is retained for full decentralization. In this work, we present a new consensus protocol named GHAST (Greedy Heaviest Adaptive Sub-Tree) which organizes blocks in a Tree-Graph structure (i.e., a directed acyclic graph (DAG) with a tree embedded) that allows fast and concurrent block generation. GHAST protocol simultaneously achieves a logarithmically bounded liveness guarantee and low confirmation latency. More specifically, for maximum latency $d$ and adversarial computing power bounded away from 50\%, GHAST guarantees confirmation with confidence $\ge 1-\varepsilon$ after a time period of $O(d\cdot \log(1/\varepsilon))$. When there is no observable attack, GHAST only needs $3d$ time to achieve confirmation at the same confidence level as six-block-confirmation in Bitcoin, while it takes roughly $360d$ in Bitcoin.

cs.CR↗
arXiv2020

Gleason Score Prediction using Deep Learning in Tissue Microarray Image

Prostate cancer (PCa) is one of the most common cancers in men around the world. The most accurate method to evaluate lesion levels of PCa is microscopic inspection of stained biopsy tissue and estimate the Gleason score of tissue microarray (TMA) image by expert pathologists. However, it is time-consuming for pathologists to identify the cellular and glandular patterns for Gleason grading in large TMA images. We used Gleason2019 Challenge dataset to build a convolutional neural network (CNN) model to segment TMA images to regions of different Gleason grades and predict the Gleason score according to the grading segmentation. We used a pre-trained model of prostate segmentation to increase the accuracy of the Gleason grade segmentation. The model achieved a mean Dice of 75.6% on the test cohort and ranked 4th in the Gleason2019 Challenge with a score of 0.778 combined of Cohen's kappa and the f1-score.

eess.IV↗
arXiv2020

Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning

Many IoT applications at the network edge demand intelligent decisions in a real-time manner. The edge device alone, however, often cannot achieve real-time edge intelligence due to its constrained computing resources and limited local data. To tackle these challenges, we propose a platform-aided collaborative learning framework where a model is first trained across a set of source edge nodes by a federated meta-learning approach, and then it is rapidly adapted to learn a new task at the target edge node, using a few samples only. Further, we investigate the convergence of the proposed federated meta-learning algorithm under mild conditions on node similarity and the adaptation performance at the target edge. To combat against the vulnerability of meta-learning algorithms to possible adversarial attacks, we further propose a robust version of the federated meta-learning algorithm based on distributionally robust optimization, and establish its convergence under mild conditions. Experiments on different datasets demonstrate the effectiveness of the proposed Federated Meta-Learning based framework.

cs.LG↗
arXiv2020

Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

eess.IV↗
arXiv2020

Magnitude and Spatial Distribution Control of the Supercurrent in Bi2O2Se-Based Josephson Junction

Many proposals in exploring topological quantum computation are based on superconducting quantum devices constructed on materials with strong spin-orbit coupling (SOC). For these devices, a full control on both the magnitude and the spatial distribution of the supercurrent would be highly demanded, but has been elusive up to now. We constructed proximity-type Josephson junction on nanoplates of Bi2O2Se, a new emerging semiconductor with strong SOC. Through electrical gating, we show that the supercurrent can be fully turned ON and OFF, and its real-space pathways can be configured either through the bulk or along the edges. Our work demonstrates Bi2O2Se as a promising platform for constructing multifunctional hybrid superconducting devices as well as for searching for topological superconductivity.

cond-mat.mes-hall↗
arXiv2020

Enhanced carrier mobility in anisotropic 2D tetrahex-carbon through strain engineering

A recently predicted two dimensional (2D) carbon allotrope, tetrahex-carbon consisting of tetragonal and hexagonal rings, draws research interests due to its unique mechanical and electronic properties. Tetrahex-C shows ultrahigh strength, negative Poisson ratio, a direct band gap and high carrier mobility. In this work, we employ first-principles density-functional theory calculations to explore the directional dependence of electronic properties such as carrier effective mass and mobility in tetrahex-C. Tetrahex-C demonstrates strong anisotropicity in effective mass of charge carrier and therefore its mobility (electric conductance) exhibits a strong orientation preference. More interesting, we find that such unique anisotropic carrier effective mass and mobility can be controlled by simple uniaxial strain. The orientation dependence of effective mass can be dramatically rotated by 90 degrees through applying uniaxial tensile strain beyond ~ 7% (11%) in the armchair direction for the hole (electron). As a result, the intrinsic carrier mobility in tetrahex-C is significantly enhanced. The results are useful for potential electronic and mechanical applications in tetrahex-C.

cond-mat.mtrl-sci↗
arXiv2020

Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0.27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

eess.IV↗
arXiv2020

Self-formed 2D/3D Heterostructure on the Edge of 2D Ruddlesden-Popper Hybrid Perovskites Responsible for Intriguing Optoelectronic Properties and Higher Cell Efficiency

The observation of low energy edge photoluminescence and its beneficial effect on the solar cell efficiency of Ruddlesden-Popper perovskites has unleashed an intensive research effort to reveal its origin. This effort, however, has been met with more challenges as the underlying material structure has still not been identified; new modellings and observations also do not seem to converge. Using 2D (BA)2(MA)2Pb3Br10 as an example, we show that 3D MAPbBr3 is formed due to the loss of BA on the edge. This self-formed MAPbBr3 can explain the reported edge emission under various conditions, while the reported intriguing optoelectronic properties such as fast exciton trapping from the interior 2D perovskite, rapid exciton dissociation and long carrier lifetime can be understood via the self-formed 2D/3D lateral perovskite heterostructure. The 3D perovskite is identified by submicron infrared spectroscopy, the emergence of XRD signature from freezer-milled nanometer-sized 2D perovskite and its photoluminescence response to external hydrostatic pressure. The revelation of this edge emission mystery and the identification of a self-formed 2D/3D heterostructure provide a new approach to engineering 2D perovskites for high-performance optoelectronic devices.

physics.app-ph↗
arXiv2020

X-CIGALE: fitting AGN/galaxy SEDs from X-ray to infrared

CIGALE is a powerful multiwavelength spectral energy distribution (SED) fitting code for extragalactic studies. However, the current version of CIGALE is not able to fit X-ray data, which often provide unique insights into AGN intrinsic power. We develop a new X-ray module for CIGALE, allowing it to fit SEDs from the X-ray to infrared (IR). We also improve the AGN fitting of CIGALE from UV-to-IR wavelengths. We implement a modern clumpy two-phase torus model, SKIRTOR. To account for moderately extincted type 1 AGNs, we implement polar-dust extinction. We publicly release the source code (named X-CIGALE). We test X-CIGALE with X-ray detected AGNs in SDSS, COSMOS, and AKARI-NEP. The fitting quality (as indicated by reduced $χ^2$) is good in general, indicating that X-CIGALE is capable of modelling the observed SED from X-ray to IR. We discuss constrainability and degeneracy of model parameters in the fitting of AKARI-NEP, for which excellent mid-IR photometric coverage is available. We also test fitting a sample of AKARI-NEP galaxies for which only X-ray upper limits are available from Chandra observations, and find that the upper limit can effectively constrain the AGN SED contribution for some systems. Finally, using X-CIGALE, we assess the ability of Athena to constrain the AGN activity in future extragalactic studies.

astro-ph.GA↗
arXiv2020

Valley polarization and valleyresistance in monolayer transition metal dichalcogenides superlattice

Manipulating the valley degree of freedom to encode information for potential valleytronic devices has ignited a new direction in solid-state physics. A significant, fundamental challenge in the field of valleytronics is how to generate and regulate valley-polarized currents by practical ways. Here, we discover a new mechanism of producing valley polarization in a monolayer transition metal dichalcogenides superlattice, in which valley-resolved gaps are formed at the supercell Brillouin zone boundaries and centers due to the intervalley scattering. When the energy of the incident electron is in the gaps, the available states are valley polarized, thus providing a valley-polarized current from the superlattice. We show that the direction and strength of the valley polarization may further be tuned by varying the potential applied the superlattice. The transmission can have a net valley polarization of 55% for a 4-period heterojunction. Moreover, such two valley filters in series may function as an electrostatically controlled giant valleyresistance device, representing a zero magnetic field counterpart to the familiar giant magnetoresistance device.

cond-mat.mes-hall↗
arXiv2020

Molecular Patterning and Directed Self-Assembly of Gold Nanoparticles on GaAs

The ability to create micro/nano patterns of organic self-assembled monolayers on semiconductor surfaces is crucial for fundamental studies and applications in a number of emerging fields in nanoscience. Here, we demonstrate the patterning of thiol molecular SAMs on oxide-free GaAs surface by dip-pen nanolithography and micro-contact printing, facilitated by a process of surface etching and passivation of the GaAs. A quantitative analysis on the molecular diffusion on GaAs was conducted by examining the writing of nanoscale dot and line patterns by DPN, which agrees well with surface diffusion models. The functionality of the patterned thiol molecules was demonstrated by directed self-assembly of gold nanoparticles onto a template of 4-Aminothiophenol SAM on GaAs. The highly selective assembly of the Au NPs was evidenced with atomic force microscopy and scanning electron microscopy. The ability to precisely control the assembly of Au NPs on oxide-free semiconductor surfaces using molecular templates may lead to an efficient bottom-up method for the fabrication of nano-plasmonic structures.

physics.app-ph↗