arXiv subjects

Qi Wang

Publications and source records attributed to Qi Wang.

At least 595 records · Page 33Linked to original sources

arXiv2020

iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting full-patient phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both the XCAT (n=50) and an independent clinical (n=10) dataset with similar accuracy. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed less than 10% dose errors for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). Conclusion: iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry. Significance: The new framework brings the creation and application of CHPs to the level of individual CHPs through automation, achieving a wider and precise organ localization, paving the way for clinical monitoring, and personalized optimization, and large-scale research.

physics.med-ph↗

arXiv2020

NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization

In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc. Many Convolutional Neural Networks (CNN) are designed for tackling this task. However, currently released datasets are so small-scale that they can not meet the needs of the supervised CNN-based algorithms. To remedy this problem, we construct a large-scale congested crowd counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range (0~20,033). Besides, a benchmark website is developed for impartially evaluating the different methods, which allows researchers to submit the results of the test set. Based on the proposed dataset, we further describe the data characteristics, evaluate the performance of some mainstream state-of-the-art (SOTA) methods, and analyze the new problems that arise on the new data. What's more, the benchmark is deployed at \url{https://www.crowdbenchmark.com/}, and the dataset/code/models/results are available at \url{https://gjy3035.github.io/NWPU-Crowd-Sample-Code/}.

cs.CV↗

arXiv2020

Pixel-wise Crowd Understanding via Synthetic Data

Crowd analysis via computer vision techniques is an important topic in the field of video surveillance, which has wide-spread applications including crowd monitoring, public safety, space design and so on. Pixel-wise crowd understanding is the most fundamental task in crowd analysis because of its finer results for video sequences or still images than other analysis tasks. Unfortunately, pixel-level understanding needs a large amount of labeled training data. Annotating them is an expensive work, which causes that current crowd datasets are small. As a result, most algorithms suffer from over-fitting to varying degrees. In this paper, take crowd counting and segmentation as examples from the pixel-wise crowd understanding, we attempt to remedy these problems from two aspects, namely data and methodology. Firstly, we develop a free data collector and labeler to generate synthetic and labeled crowd scenes in a computer game, Grand Theft Auto V. Then we use it to construct a large-scale, diverse synthetic crowd dataset, which is named as "GCC Dataset". Secondly, we propose two simple methods to improve the performance of crowd understanding via exploiting the synthetic data. To be specific, 1) supervised crowd understanding: pre-train a crowd analysis model on the synthetic data, then fine-tune it using the real data and labels, which makes the model perform better on the real world; 2) crowd understanding via domain adaptation: translate the synthetic data to photo-realistic images, then train the model on translated data and labels. As a result, the trained model works well in real crowd scenes.

cs.CV↗

arXiv2020

Seamless maps of major elements of the Moon: Results from high-resolution geostationary satellite

Major elements such as Fe, Ti, Mg, Al, Ca, and Si play very important roles in understanding the origin and evolution of the Moon. Previous maps of these major elements derived from orbital data are based on mosaic images or low-resolution Gamma ray data. The hue variations and gaps among orbital boundaries in the mosaic images are not conducive to geological studies. This paper aims to produce seamless and homogenous distribution maps of major elements using the single-exposure image of the whole lunar disk obtained by China's high-resolution geostationary satellite, Gaofen-4, with a spatial resolution of ~500 m. The elemental contents of soil samples returned by Apollo and Luna missions were used as ground truth, and were correlated with the reflectance of the sampling sites extracted from Gaofen-4 data. The final distribution maps of these major oxides are generated with the statistical regression model. With these products the average contents and proportions of the major elements for maria and highlands were estimated and compared. The results showed that SiO2 and TiO2 have the highest and lowest fractions in mare and highland areas, respectively. Besides, the relative concentrations of these elements could serve as indicators of geologic processes, e.g., the obviously asymmetric distributions of Al2O3, CaO, and SiO2 around Tycho crater may suggest that Tycho crater was formed by an oblique impact from the southwest direction.

astro-ph.EP↗

arXiv2020

Spin-orbit quantum impurity and quantization in a topological magnet

Quantum states induced by single-atomic impurities are at the frontier of physics and material science. While such states have been reported in high-temperature superconductors and dilute magnetic semiconductors, they are unexplored in topological magnets which can feature spin-orbit tunability. Here we use spin-polarized scanning tunneling microscopy/spectroscopy (STM/S) to study the engineered quantum impurity in a topological magnet Co3Sn2S2. We find that each substituted In impurity introduces a striking localized bound state. Our systematic magnetization-polarized probe reveals that this bound state is spin-down polarized, in lock with a negative orbital magnetization. Moreover, the magnetic bound states of neighboring impurities interact to form quantized orbitals, exhibiting an intriguing spin-orbit splitting, analogous to the splitting of the topological fermion line. Our work collectively demonstrates the strong spin-orbit effect of the single-atomic impurity at the quantum level, suggesting that a nonmagnetic impurity can introduce spin-orbit coupled magnetic resonance in topological magnets.

cond-mat.mtrl-sci↗

arXiv2020

A metasurface composed of 3-bit coding linear polarization conversion elements and its application to RCS reduction of patch antenna

In this paper, a low radar cross section (RCS) patch antenna based on the 3-bit metasurface composed of linear polarization conversion elements is designed. At first, 3-bit coding metamaterials are constructed by a sequence of eight coded unit cells, which have a similar cross-polarized reflected amplitude response and gradient reflected phase responses covering 0~2π, respectively. Equivalent circuit models (ECMs) of these unit cells are created to describe their electrical behavior for the two linear incident polarizations at the same time. Then, a patch antenna is integrated on the 3-bit metasurface, of which the elements are placed with a 2-dimensional linear coding sequence. The metal square ring is set around the patch antenna to protect it from the disturbance of metasurface. Both the simulation and experiment results demonstrate that the designed metasurface can primarily reduce the antenna RCS at a broadband, while the antenna performances are not degraded significantly.

physics.app-ph↗

arXiv2020

Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos

Many semantic events in team sport activities e.g. basketball often involve both group activities and the outcome (score or not). Motion patterns can be an effective means to identify different activities. Global and local motions have their respective emphasis on different activities, which are difficult to capture from the optical flow due to the mixture of global and local motions. Hence it calls for a more effective way to separate the global and local motions. When it comes to the specific case for basketball game analysis, the successful score for each round can be reliably detected by the appearance variation around the basket. Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos. Firstly, an algorithm is proposed to estimate the global motions from the mixed motions based on the intrinsic property of camera adjustments. And the local motions could be obtained from the mixed and global motions. Secondly, a two-stream 3D CNN framework is utilized for group activity recognition over the separated global and local motion patterns. Thirdly, the basket is detected and its appearance features are extracted through a CNN structure. The features are utilized to predict the success or failure. Finally, the group activity recognition and success/failure prediction results are integrated using the kronecker product for event recognition. Experiments on NCAA dataset demonstrate that the proposed method obtains state-of-the-art performance.

cs.CV↗

arXiv2020

Fermion-boson many-body interplay in a frustrated kagome paramagnet

Kagome-net, appearing in areas of fundamental physics, materials, photonic and cold-atom systems, hosts frustrated fermionic and bosonic excitations. However, it is extremely rare to find a system to study both fermionic and bosonic modes to gain insights into their many-body interplay. Here we use state-of-the-art scanning tunneling microscopy and spectroscopy to discover unusual electronic coupling to flat-band phonons in a layered kagome paramagnet. Our results reveal the kagome structure with unprecedented atomic resolution and observe the striking bosonic mode interacting with dispersive kagome electrons near the Fermi surface. At this mode energy, the fermionic quasi-particle dispersion exhibits a pronounced renormalization, signaling a giant coupling to bosons. Through a combination of self-energy analysis, first-principles calculation, and a lattice vibration model, we present evidence that this mode arises from the geometrically frustrated phonon flat-band, which is the lattice analog of kagome electron flat-band. Our findings provide the first example of kagome bosonic mode (flat-band phonon) in electronic excitations and its strong interaction with fermionic degrees of freedom in kagome-net materials.

cond-mat.mtrl-sci↗

arXiv2020

Predicting the propensity for thermally activated $β$ events in metallic glasses via interpretable machine learning

The elementary excitations in metallic glasses (MGs), i.e., $β$ processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated $β$ processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation would induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at an unprecedented accuracy over ML models for stress-driven activation events. Interestingly, a quantitative "between-task" transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

cond-mat.mtrl-sci↗

arXiv2020

Orbital-selective Dirac fermions and extremely flat bands in frustrated kagome-lattice metal CoSn

Layered kagome-lattice 3d transition metals are emerging as an exciting platform to explore the frustrated lattice geometry and quantum topology. However, the typical kagome electronic bands, characterized by sets of the Dirac-like band capped by a phase-destructive flat band, have not been clearly observed, and their orbital physics are even less well investigated. Here, we present close-to-textbook kagome bands with orbital differentiation physics in CoSn, which can be well described by a minimal tight-binding model with single-orbital hopping in Co kagome lattice. The capping flat bands with bandwidth less than 0.2 eV run through the whole Brillouin zone, especially the bandwidth of the flat band of out-of-plane orbitals is less than 0.02 eV along G-M. The energy gap induced by spin-orbit interaction at the Dirac cone of out-of-plane orbitals is much smaller than that of in-plane orbitals, suggesting orbital-selective character of the Dirac fermions.

cond-mat.mtrl-sci↗

arXiv2020

Supplementary Variable Method for Developing Structure-Preserving Numerical Approximations to Thermodynamically Consistent Partial Differential Equations

We present a new temporal discretization paradigm for developing energy-production-rate preserving numerical approximations to thermodynamically consistent partial differential equation systems, called the supplementary variable method. The central idea behind it is to introduce a supplementary variable to the thermodynamically consistent model to make the over-determined equation system, consisting of the thermodynamically consistent PDE system, the energy definition and the energy dissipation equation, structurally stable. The supplementary variable allows one to retain the consistency between the energy dissipation equation and the PDE system after the temporal discretization. We illustrate the method using a dissipative gradient flow model. Among virtually infinite many possibilities, we present two ways to add the supplementary variable in the gradient flow model to develop energy-dissipation-rate preserving algorithms. Spatial discretizations are carried out using the pseudo-spectral method. We then compare the two new schemes with the energy stable SAV scheme and the fully implicit Crank-Nicolson scheme. The results favor the new schemes in the overall performance. This new numerical paradigm can be applied to any thermodynamically consistent models.

math.NA↗

arXiv2020

Report on the finiteness of silting objects

We discuss the finiteness of (two-term) silting objects. First, we investigate new triangulated categories without silting object. Second, one studies two classes of $τ$-tilting-finite algebras and give the numbers of their two-term silting objects. Finally, we explore when $τ$-tilting-finiteness implies representatoin-finiteness, and obtain several classes of algebras in which a $τ$-tilting-finite algebra is representation-finite.

math.RT↗

arXiv2020

An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions

Stochastic differential equations (SDEs) or diffusions are continuous-valued continuous-time stochastic processes widely used in the applied and mathematical sciences. Simulating paths from these processes is usually an intractable problem, and typically involves time-discretization approximations. We propose an exact Markov chain Monte Carlo sampling algorithm that involves no such time-discretization error. Our sampler is applicable to the problem of prior simulation from an SDE, posterior simulation conditioned on noisy observations, as well as parameter inference given noisy observations. Our work recasts an existing rejection sampling algorithm for a class of diffusions as a latent variable model, and then derives an auxiliary variable Gibbs sampling algorithm that targets the associated joint distribution. At a high level, the resulting algorithm involves two steps: simulating a random grid of times from an inhomogeneous Poisson process, and updating the SDE trajectory conditioned on this grid. Our work allows the vast literature of Monte Carlo sampling algorithms from the Gaussian process literature to be brought to bear to applications involving diffusions. We study our method on synthetic and real datasets, where we demonstrate superior performance over competing methods.

stat.CO↗

arXiv2020

Localized spin-orbit polaron in magnetic Weyl semimetal Co$_3$Sn$_2$S$_2$

The kagome lattice Co$_3$Sn$_2$S$_2$ exhibits the quintessential topological phenomena of a magnetic Weyl semimetal such as the chiral anomaly and Fermi-arc surface states. Probing its magnetic properties is crucial for understanding this correlated topological state. Here, using spin-polarized scanning tunneling microscopy/spectroscopy (STM/S), we report the discovery of localized spin-orbit polarons (SOPs) with three-fold rotation symmetry nucleated around single-S vacancies in Co$_3$Sn$_2$S$_2$. The SOPs carry a spin-polarized magnetic moment and a large orbital magnetization of a topological origin associated with the Berry phase and the persistent circulating current. Appreciable magneto-elastic coupling of the SOP is detected by atomic force microscope and STM. Our findings suggest that the SOPs can enhance magnetism and stability of the magnetic Weyl nodes for more robust time-reversal-symmetry-breaking topological phenomena. Controlled engineering of the SOPs may pave the way toward practical applications in functional quantum devices.

cond-mat.mtrl-sci↗

arXiv2020

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The non-incremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

cond-mat.mtrl-sci↗

arXiv2020

Multiple states in turbulent large-aspect ratio thermal convection: What determines the number of convection rolls?

Recent findings suggest that wall-bounded turbulent flow can take different statistically stationary turbulent states, with different transport properties, even for the very same values of the control parameters. What state the system takes depends on the initial conditions. Here we analyze the multiple states in large-aspect ratio ($Γ$) two-dimensional turbulent Rayleigh--Bénard flow with no-slip plates and horizontally periodic boundary conditions as model system. We determine the number $n$ of convection rolls, their mean aspect ratios $Γ_r = Γ/n$, and the corresponding transport properties of the flow (i.e., the Nusselt number $Nu$), as function of the control parameters Rayleigh ($Ra$) and Prandtl number. The effective scaling exponent $β$ in $Nu \sim Ra^β$ is found to depend on the realized state and thus $Γ_r$, with a larger value for the smaller $Γ_r$. By making use of a generalized Friedrichs inequality, we show that the elliptical instability and viscous damping determine the $Γ_r$-window for the realizable turbulent states. The theoretical results are in excellent agreement with our numerical finding $2/3 \le Γ_r \le 4/3$, where the lower threshold is approached for the larger $Ra$. Finally, we show that the theoretical approach to frame $Γ_r$ also works for free-slip boundary conditions.

physics.flu-dyn↗

arXiv2020

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.

cond-mat.mtrl-sci↗

arXiv2020

From zonal flow to convection rolls in Rayleigh-Bénard convection with free-slip plates

Rayleigh-Bénard (RB) convection with free-slip plates and horizontally periodic boundary conditions is investigated using direct numerical simulations. Two configurations are considered, one is two-dimension (2D) RB convection and the other one three-dimension (3D) RB convection with a rotating axis parallel to the plate. We explore the parameter range of Rayleigh numbers Ra from $10^7 to $10^9$ and Prandtl numbers $Pr$ from $1$ to $100$. We show that zonal flow, which was observed, for example, by Goluskin \emph{et al}. \emph{J. Fluid. Mech.} 759, 360-385 (2014) for $Γ=2$, is only stable when $Γ$ is smaller than a critical value, which depends on $Ra$ and $Pr$. With increasing $Γ$, we find a second regime in which both zonal flow and different convection roll states can be statistically stable. For even larger $Γ$, in a third regime, only convection roll states are statistically stable and zonal flow is not sustained. For the 3D simulations, we fix $Ra=10^7$ and $Pr=0.71$, and compare the flow for $Γ=8$ and $Γ= 16$. We demonstrate that with increasing aspect ratio $Γ$, zonal flow, which was observed for small $Γ=2π$ by von Hardenberg \emph{et al}. \emph{Phys. Rev. Lett.} 15, 134501 (2015), completely disappears for $Γ=16$. For such large $Γ$ only convection roll states are statistically stable. In between, here for medium aspect ratio $Γ= 8$, the convection roll state and the zonal flow state are both statistically stable. What state is taken depends on the initial conditions, similarly as we found for the 2D case.

physics.flu-dyn↗