arXiv subjects

Guang Yang

Publications and source records attributed to Guang Yang.

At least 505 records · Page 28Linked to original sources

arXiv2019

Piercing Through Highly Obscured and Compton-thick AGNs in the Chandra Deep Fields: I. X-ray Spectral and Long-term Variability Analyses

We present a detailed X-ray spectral analysis of 1152 AGNs selected in the Chandra Deep Fields (CDFs), in order to identify highly obscured AGNs ($N_{\rm H} > 10^{23}\ \rm cm^{-2}$). By fitting spectra with physical models, 436 (38%) sources with $L_{\rm X} > 10^{42}\ \rm erg\ s^{-1}$ are confirmed to be highly obscured, including 102 Compton-thick (CT) candidates. We propose a new hardness-ratio measure of the obscuration level which can be used to select highly obscured AGN candidates. The completeness and accuracy of applying this method to our AGNs are 88% and 80%, respectively. The observed logN-logS relation favors cosmic X-ray background models that predict moderate (i.e., between optimistic and pessimistic) CT number counts. 19% (6/31) of our highly obscured AGNs that have optical classifications are labeled as broad-line AGNs, suggesting that, at least for part of the AGN population, the heavy X-ray obscuration is largely a line-of-sight effect, i.e., some high-column-density clouds on various scales (but not necessarily a dust-enshrouded torus) along our sightline may obscure the compact X-ray emitter. After correcting for several observational biases, we obtain the intrinsic NH distribution and its evolution. The CT-to-highly-obscured fraction is roughly 52% and is consistent with no evident redshift evolution. We also perform long-term (~17 years in the observed frame) variability analyses for 31 sources with the largest number of counts available. Among them, 17 sources show flux variabilities: 31% (5/17) are caused by the change of NH, 53% (9/17) are caused by the intrinsic luminosity variability, 6% (1/17) are driven by both effects, and 2 are not classified due to large spectral fitting errors.

astro-ph.GA↗

arXiv2019

Evidence of Majorana Zero Modes in Josephson Trijunctions

In search of fault-tolerant topological quantum computation (TQC), zero-bias conductance peak as a necessary signature of Majorana zero modes (MZMs) has been observed in a number of solid-state systems. Here, we present the signature of MZMs from a phase-sensitive experiment on Josephson trijunctions constructed on the surface of three-dimensional topological insulators. We observed that the minigap at the center of the trijunction is protected to close over extended regions in phase space, supporting in principle the Majorana phase diagram proposed by Fu and Kane in 2008. Our study paves the way for further braiding MZMs and exploring TQC on a scalable two-dimensional platform.

cond-mat.mes-hall↗

arXiv2019

A Machine Learning Approach to Shipping Box Design

Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely challenging operations task to strategically select the best combination of tens of box sizes from thousands of feasible ones to be responsible for hundreds of thousands of orders daily placed on millions of inventory products. In this paper, we present a machine learning approach to tackle the task by formulating the box design problem prescriptively as a generalized version of weighted $k$-medoids clustering problem, where the parameters are estimated through a variety of descriptive analytics. We test this machine learning approach on fulfillment data collected from Walmart U.S. eCommerce, and our approach is shown to be capable of improving the box utilization rate by more than $10\%$.

stat.ML↗

arXiv2019

Galaxies of the $z \sim 2$ Universe. I. Grism-Selected Rest-Frame Optical Emission Line Galaxies

Euclid, WFIRST, and HETDEX will make emission-line selected galaxies the largest observed constituent in the $z > 1$ universe. However, we only have a limited understanding of the physical properties of galaxies selected via their Ly$α$ or rest-frame optical emission lines. To begin addressing this problem, we present the basic properties of $\sim 2,000$ AEGIS, COSMOS, GOODS-N, GOODS-S, and UDS galaxies identified in the redshift range $1.90 < z < 2.35$ via their [O II], H$β$, and [O III] emission lines. For these $z \sim 2$ galaxies, [O III] is generally much brighter than [O II] and H$β$, with typical rest-frame equivalent widths of several hundred Angstroms. Moreover, these strong emission-line systems span an extremely wide range of stellar mass ($\sim 3$ dex), star-formation rate ($\sim 2$ dex), and [O III] luminosity ($\sim 2$ dex). Comparing the distributions of these properties to those of continuum selected galaxies, we find that emission-line galaxies have systematically lower stellar masses and lower optical/UV dust attenuations. These measurements lay the groundwork for an extensive comparison between these rest-frame optical emission-line galaxies and Ly$α$ emitters identified in the HETDEX survey.

astro-ph.GA↗

arXiv2019

Towards Practical Visual Search Engine within Elasticsearch

In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine. As far as we know, this is the first time such a system has been implemented in eCommerce, and our efforts have turned out to be highly worthwhile. We end up with a novel and exciting visual search solution that is extremely easy to be deployed, distributed, scaled and monitored in a cost-friendly manner. Moreover, our platform is intrinsically flexible in supporting multimodal searches, where visual and textual information can be jointly leveraged in retrieval. The core idea is to encode image feature vectors into a collection of string tokens in a way such that closer vectors will share more string tokens in common. By doing that, we can utilize Elasticsearch to efficiently retrieve similar images based on similarities within encoded sting tokens. As part of the development, we propose a novel vector to string encoding method, which is shown to substantially outperform the previous ones in terms of both precision and latency. First-hand experiences in implementing this Elasticsearch-based platform are extensively addressed, which should be valuable to practitioners also interested in building visual search engine on top of Elasticsearch.

cs.CV↗

arXiv2019

Continuous-time Signal Temporal Logic Planning with Control Barrier Function

Temporal Logic (TL) guided control problems have gained interests in recent years. By using the TL, one can specify a wide range of temporal constraints on the system and is widely used in cyber-physical systems. On the other hand, Control Barrier Functions have also gained interests in the context of safety critical applications. However, most of the existing approaches only focus on discrete-time dynamical systems. In this paper, we propose an offline trajectory planner for linear systems subject to safety and temporal specifications. Such specifications can be expressed as logical junctions or disjunctions of linear CBFs, or as STL specifications with linear predicates. Our planner produces trajectories that are valid in continuous time, while assuming only discrete-time control updates and arbitrary time interval in the STL formula. Our planner is based on a Mixed Integer Quadratic Programming (MIQP) formulation, where the linear STL predicates are encoded as set of linear constraints to guarantee satisfaction at on a finite discrete set of time instants, while we use CBFs to derive constraints that guarantee continuous satisfaction between time instants. Moreover, we have shown the predicates can be encoded as time-based CBF constraints for system with any relative degrees. We validate our theoretical results and formulation through numerical simulations.

eess.SY↗

arXiv2019

Self-triggered Control for Safety Critical Systems using Control Barrier Functions

We propose a real-time control strategy that combines self-triggered control with Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF). Similar to related works proposing CLF-CBF-based controllers, the computation of the controller is achieved by solving a Quadratic Program (QP). However, we propose a Zeroth-Order Hold (ZOH) implementation of the controller that overcomes the main limitations of traditional approaches based on periodic controllers, i.e., unnecessary controller updates and potential violations of the safety constraints. Central to our approach is the novel notion of safe period, which enforces a strong safety guarantee for implementing ZOH control. In addition, we prove that the system does not exhibit a Zeno behavior as it approaches the desired equilibrium.

eess.SY↗

arXiv2019

Evident black hole-bulge coevolution in the distant universe

Observations in the local universe show a tight correlation between the masses of supermassive black holes (SMBHs; $M_{\rm BH}$) and host-galaxy bulges ($M_{\rm bulge}$), suggesting a strong connection between SMBH and bulge growth. However, direct evidence for such a connection in the distant universe remains elusive. We have studied sample-averaged SMBH accretion rate ($\overline{\rm BHAR}$) for bulge-dominated galaxies at $z=0.5-3$. While previous observations found $\overline{\rm BHAR}$ is strongly related to host-galaxy stellar mass ($M_\star$) for the overall galaxy population, our analyses show that, for the bulge-dominated population, $\overline{\rm BHAR}$ is mainly related to SFR rather than $M_\star$. This ${\overline{\rm BHAR}}$-SFR relation is highly significant, e.g. $9.0σ$ (Pearson statistic) at $z=0.5-1.5$. Such a $\overline{\rm BHAR}$-SFR connection does not exist among our comparison sample of galaxies that are not bulge-dominated, for which $M_\star$ appears to be the main determinant of SMBH accretion. This difference between the bulge-dominated and comparison samples indicates that SMBHs only coevolve with bulges rather than the entire galaxies, explaining the tightness of the local $M_{\rm BH}$-$M_{\rm bulge}$ correlation. Our best-fit $\overline{\rm BHAR}$-SFR relation for the bulge-dominated sample is $\log\overline{\rm BHAR} = \log\mathrm{SFR} - (2.48\pm0.05)$ (solar units). The best-fit $\overline{\rm BHAR}/\mathrm{SFR}$ ratio ($10^{-2.48}$) for bulge-dominated galaxies is similar to the observed $M_{\rm BH}/M_{\rm bulge}$ values in the local universe. Our results reveal that SMBH and bulge growth are in lockstep, and thus non-causal scenarios of merger averaging are unlikely the origin of the $M_{\rm BH}$-$M_{\rm bulge}$ correlation. This lockstep growth also predicts that the $M_{\rm BH}$-$M_{\rm bulge}$ relation should not have strong redshift dependence.

astro-ph.GA↗

arXiv2019

Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have employed fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 +- 0.033 and mean Dice score of 0.702 +- 0.071 for LA scar quantification. Compared with the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be useful in diagnosis and prognosis of AF.

cs.CV↗

arXiv2019

Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets was limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including the provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (\url{www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mmwhs/}).

cs.CV↗

arXiv2019

Revisiting Skip-Gram Negative Sampling Model with Rectification

We revisit skip-gram negative sampling (SGNS), one of the most popular neural-network based approaches to learning distributed word representation. We first point out the ambiguity issue undermining the SGNS model, in the sense that the word vectors can be entirely distorted without changing the objective value. To resolve the issue, we investigate the intrinsic structures in solution that a good word embedding model should deliver. Motivated by this, we rectify the SGNS model with quadratic regularization, and show that this simple modification suffices to structure the solution in the desired manner. A theoretical justification is presented, which provides novel insights into quadratic regularization . Preliminary experiments are also conducted on Google's analytical reasoning task to support the modified SGNS model.

cs.CL↗

arXiv2019

How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach

Single image super-resolution (SISR) is of great importance as a low-level computer vision task. The fast development of Generative Adversarial Network (GAN) based deep learning architectures realises an efficient and effective SISR to boost the spatial resolution of natural images captured by digital cameras. However, the SISR for medical images is still a very challenging problem. This is due to (1) compared to natural images, in general, medical images have lower signal to noise ratios, (2) GAN based models pre-trained on natural images may synthesise unrealistic patterns in medical images which could affect the clinical interpretation and diagnosis, and (3) the vanilla GAN architecture may suffer from unstable training and collapse mode that can also affect the SISR results. In this paper, we propose a novel lesion focused SR (LFSR) method, which incorporates GAN to achieve perceptually realistic SISR results for brain tumour MRI images. More importantly, we test and make comparison using recently developed GAN variations, e.g., Wasserstein GAN (WGAN) and WGAN with Gradient Penalty (WGAN-GP), and propose a novel multi-scale GAN (MS-GAN), to achieve a more stabilised and efficient training and improved perceptual quality of the super-resolved results. Based on both quantitative evaluations and our designed mean opinion score, the proposed LFSR coupled with MS-GAN has performed better in terms of both perceptual quality and efficiency.

eess.IV↗

arXiv2018

The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation

Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability of labeled data and motion artifacts from cardiac imaging. In this work, we present an iterative segmentation algorithm for LV delineation. By coupling deep learning with a novel dynamic-based labeling scheme, we present a new methodology where a policy model is learned to guide an agent to travel over the the image, tracing out a boundary of the ROI -- using the magnitude difference of the Poincaré map as a stopping criterion. Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge. Our method outperforms the previous research over many metrics. In order to demonstrate the transferability of our method we present encouraging results over the STACOM 2011 data, when using a model trained on the SCD dataset.

cs.CV↗

arXiv2018

Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images

We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session. We formulate the joint distribution of images using a multivariate mixture model (MvMM), and employ the maximum likelihood estimator (MLE) for texture classification of the images simultaneously. The MvMM can also embed transformations assigned to the images to correct the misregistration. The iterated conditional mode algorithm is adopted for optimization. This method first extracts the anatomical shape of the LA, and then estimates a prior probability map. It projects the resulting segmentation onto the LA surface, for quantification and analysis of scarring. We applied the proposed method to 36 clinical data sets and obtained promising results (Accuracy: $0.809\pm .150$, Dice: $0.556\pm.187$). We compared the method with the conventional algorithms and showed an evidently and statistically better performance ($p<0.03$).

cs.CV↗

arXiv2018

Atrial scars segmentation via potential learning in the graph-cuts framework

Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF). However, due to the low image quality automating the quantification and analysis of the atrial scars is challenging. In this study, we pro-posed a fully automated method based on the graph-cuts framework, where the potential of the graph is learned on a surface mesh of the left atrium (LA) using an equidistant projection and a Deep Neural Network (DNN). For validation, we employed 100 datasets with manual delineation. The results showed that the performance of the proposed method improved and converged with respect to the increased size of training patches, which provide important features of the structural and texture information learned by the DNN. The segmentation could be further improved when the contribution from the t-link and n-link is balanced, thanks to inter-relationship learned by the DNN for the graph-cuts algorithm. Compared with the published methods which mostly acquired manual delineation of the LA or LA wall, our method is fully automatic and demonstrated evidently better results with statistical significance. Finally, the accuracy of quantifying the scars assessed by the Dice score was 0.570. The results are promising and the method can be useful in diagnosis and prognosis of AF.

cs.CV↗

arXiv2018

Understanding Phonon Scattering by Nano-Precipitates in Potassium-Doped Lead Chalcogenides

We present a comprehensive experimental and theoretical study of phonon scattering by nanoprecipitates in potassium-doped PbTe, PbSe and PbS. We highlight the role of the precipitate size distribution measured by microscopy, whose tuning allows for thermal conductivities lower than the limit achievable with a single size. The correlation between the size distribution and the contributions to thermal conductivity from phonons in different frequency ranges provides a physical basis to the experimentally measured thermal conductivities, and a criterion to estimate the lowest achievable thermal conductivity. The results have clear implications for efficiency enhancements in nanostructured bulk thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2018

Lesion Focused Super-Resolution

Super-resolution (SR) for image enhancement has great importance in medical image applications. Broadly speaking, there are two types of SR, one requires multiple low resolution (LR) images from different views of the same object to be reconstructed to the high resolution (HR) output, and the other one relies on the learning from a large amount of training datasets, i.e., LR-HR pairs. In real clinical environment, acquiring images from multi-views is expensive and sometimes infeasible. In this paper, we present a novel Generative Adversarial Networks (GAN) based learning framework to achieve SR from its LR version. By performing simulation based studies on the Multimodal Brain Tumor Segmentation Challenge (BraTS) datasets, we demonstrate the efficacy of our method in application of brain tumor MRI enhancement. Compared to bilinear interpolation and other state-of-the-art SR methods, our model is lesion focused, which is not only resulted in better perceptual image quality without blurring, but also more efficient and directly benefit for the following clinical tasks, e.g., lesion detection and abnormality enhancement. Therefore, we can envisage the application of our SR method to boost image spatial resolution while maintaining crucial diagnostic information for further clinical tasks.

eess.IV↗

arXiv2018

Exact quantum query complexity of weight decision problems via Chebyshev polynomials

The weight decision problem, which requires to determine the Hamming weight of a given binary string, is a natural and important problem, with applications in cryptanalysis, coding theory, fault-tolerant circuit design and so on. In particular, both Deutsch-Jozsa problem and Grover search problem can be interpreted as special cases of weight decision problems. In this work, we investigate the exact quantum query complexity of weight decision problems, where the quantum algorithm must always output the correct answer. More specifically we consider a partial Boolean function which distinguishes whether the Hamming weight of the length-$n$ input is $k$ or it is $l$. Our contribution includes both upper bounds and lower bounds for the precise number of queries. Furthermore, for most choices of $(\frac{k}{n},\frac{l}{n})$ and sufficiently large $n$, the gap between our upper and lower bounds is no more than one. To get the results, we first build the connection between Chebyshev polynomials and our problem, then determine all the boundary cases of $(\frac{k}{n},\frac{l}{n})$ with matching upper and lower bounds, and finally we generalize to other cases via a new \emph{quantum padding} technique. This quantum padding technique can be of independent interest in designing other quantum algorithms.

quant-ph↗