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Peng Gao

Publications and source records attributed to Peng Gao.

At least 415 records · Page 23Linked to original sources
arXiv2019

Dual-energy CT imaging using a single-energy CT data is feasible via deep learning

In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is, therefore, challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as single-energy CT (SECT) scanners. Here we develop a deep learning approach to perform DECT imaging by using standard SECT data. A deep learning model to map low-energy image to high-energy image using a two-stage convolutional neural network (CNN) is developed. The model was evaluated using patients who received contrast-enhanced abdomen DECT scan with a popular DE application: virtual non-contrast (VNC) imaging and contrast quantification. The HU differences between the predicted and original high-energy CT images are 3.47, 2.95, 2.38 and 2.40 HU for ROIs on the spine, aorta, liver, and stomach, respectively. The HU differences between VNC images obtained from original DECT and deep learning DECT are 4.10, 3.75, 2.33 and 2.92 HU for the 4 ROIs, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9\%, suggesting high consistency between the predicted and the original high-energy CT images. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost.

physics.med-ph↗
arXiv2019

Weighted first moments of some special quadratic Dirichlet $L$-functions

In this paper, we obtain asymptotic formulas for weighted first moments of central values of families of primitive quadratic Dirichlet $L$-functions whose conductors comprise only primes that split in a given quadratic number field. We then deduce a non-vanishing result of these $L$-functions at the point $s=1/2$.

math.NT↗
arXiv2019

MATGANIP: Learning to Discover the Structure-Property Relationship in Perovskites with Generative Adversarial Networks

Accelerating the design of materials with artificial neural network draws more attention due to its magnitude potential. In the past works, some tools of materials information have been developed to promote the industrialize of state-of-the-art materials, such as the materials project, AFlow, and open quantum materials database. Else, more various endeavors are required for artificial general intelligence in the area of materials. In our works, we design neural networks named MATGANIP, which applies a combination of generative adversarial networks, graph networks, convolutional neural networks, and long short term memory for the perovskite materials. We adopt it for the building of a structure-property relationship, where the trained properties contain: the computational geometric property, tolerance factor; and the ground state property of Quantum theory, the vacuum energy. Moreover, the data-set about the ABX3 perovskites is used for the learning of tolerance factor, to extend its function of tolerance factor into the structural identification of the arrangement of disorder atoms; another data-set about the density functional theory (DFT) calculation results is for the ground state energy of quantum theory, to obtain the more accurate result than the training of DFT calculated results in similar works. In our training task of the DFT date-set, we adopt the intuitive criterion to evaluate its performance: the mean absolute error (MAE) is smaller than 0.3 meV/atom, and the mean absolute error rate (MAER) is smaller than 0.01%. These results prove the potential ability of our MATGANIP in developing the relationship between materials structure and their properties. Notably, it suits for some scenes involved massive possible structures and their properties, such as the arrangement of the atoms in the structural phase transition of the inorganic perovskites with mixed atoms.

cond-mat.dis-nn↗
arXiv2019

Atomic-environment-dependent thickness of ferroelastic domain walls

Domain walls are of increasing interest in ferroelectrics because of their unique properties and potential applications in future nanoelectronics. However, the thickness of ferroelastic domain walls remains elusive due to the challenges in experimental characterization. Here, we determine the atomic structure of ferroelastic domain walls and precisely measure the polarization and domain wall thickness at picometer scale using annular bright field imaging in an aberration-corrected scanning transmission electron microscope. We find that the domain wall thickness in PbZr0.2Ti0.8O3 and PbTiO3 thin films is typically about one-unit cell, across which the oxygen octahedron distortion behavior is in excellent agreement with first principles calculations. Remarkably, wider domain walls about two-unit cells in thickness are also observed for those domains walls are coupled with dislocations and underwent a compressive strain. These results suggest that the thickness of ferroelastic domain walls highly depends on their atomic environments. This study can help us to understand the past debatable experimental results and provide further insights into control of domain walls via strain engineering for their possible applications in nanoelectronics.

cond-mat.mtrl-sci↗
arXiv2019

Large Magnetoelectric Response in Sr2IrO4/SrTiO3 superlattices with non-equivalent interfaces

Large magnetoelectric response in thin films is highly desired for high-throughput and high-density microelectronic applications. However, the d0 rule in single-phase compounds usually results in a weak interaction between ferroelectric and magnetic orders; the magnetoelectric coupling via elastic resonance in composites restricts their thin-film integration in broadband. Here, we effectuate a concurrence of ferroelectric-like and antiferromagnetic phase transitions in Sr2IrO4/SrTiO3 superlattices by artificial design periodically non-equivalent interfaces, where a maximum magnetoelectric coefficient of ~980 mV cm-1 Oe-1 can be measured. Evidenced by synchrotron X-ray absorption and electron energy loss spectroscopies, a lopsided electron occupation occurs at the interfacial Ti ions. From perturbative calculations and numerical results, a strong coupling of antiferromagnetism and asymmetric electron occupation mediated by spin-orbit interaction leads to a large bulk magnetoelectric response. This atomic tailoring of the quantum order parameters in 3d and 5d oxides provides an alternative pathway towards strong magnetoelectric effects with thin-film integrations.

cond-mat.str-el↗
arXiv2019

Atomic Imaging of Mechanically Induced Topological Transition of Ferroelectric Vortices

Ferroelectric vortices formed through complex lattice-charge interactions have great potential in applications for future nanoelectronics such as memories. For practical applications, it is crucial to manipulate these topological states under external stimuli. Here, we apply mechanical loads to locally manipulate the vortices in a PbTiO3-SrTiO3 superlattice via atomically resolved in situ scanning transmission electron microscopy. The vortices undergo a transition to the a-domain with in-plane polarization under external compressive stress and spontaneously recover after removal of the stress. We reveal the detailed transition process at the atomic scale and reproduce this numerically using phase-field simulations. These findings provide new pathways to control the exotic topological ferroelectric structures for future nanoelectronics and also valuable insights into understanding of lattice-charge interactions at nanoscale.

cond-mat.mtrl-sci↗
arXiv2019

Nanoscale Probing of Localized Surface Phonon Polaritons in SiC Nanorods with Swift Electrons

Surface phonon polaritons hold much potential for subwavelength control and manipulation of light at the infrared to terahertz wavelengths. Here, aided by monochromatic scanning transmission electron microscopy - electron energy loss spectroscopy technique, we study the excitation of optical phonon modes in SiC nanorods. Surface phonon polaritons are modulated by the geometry and size of SiC nanorods. In particular, we study the dispersion relation, spatial dependence and geometry and size effects of surface phonon polaritons. These experimental results are in agreement with dielectric response theory and numerical simulation. Providing critical information for manipulating light in polar dielectrics, these findings should be useful for design of novel nanoscale phonon-photonic devices.

cond-mat.mes-hall↗
arXiv2019

Artificially Engineered Nanostrain in Iron Chalcogenide Superconductor Thin Film for Enhancing Supercurrent

Although nanoscale deformation, such as nanostrain in iron chalcogenide (FeSexTe1-x, FST) thin films, has attracted attention owing to the enhancement of general superconducting properties, including critical current density (Jc) and critical transition temperature, its formation has proven to be an extremely challenging and complex process thus far. Herein, we successfully fabricated an epitaxial FST thin film with uniformly distributed nanostrain by injection of a trace amount of CeO2 inside FST matrix using sequential pulsed laser deposition. Using transmission electron microscopy and geometrical phase analysis, we verified that a trace amount of CeO2 injection forms nanoscale fine defects with a nanostrained region, which has a tensile strain (ezz ~ 0.02) along the c-axis of the FST matrix. The nanostrained FST thin film achieves a remarkable Jc of 3.5 MA/cm2 for a self-field at 6 K and a highly enhanced Jc under the entire magnetic field with respect to a pristine FST thin film.

cond-mat.supr-con↗
arXiv2019

HyperService: Interoperability and Programmability Across Heterogeneous Blockchains

Blockchain interoperability, which allows state transitions across different blockchain networks, is critical functionality to facilitate major blockchain adoption. Existing interoperability protocols mostly focus on atomic token exchange between blockchains. However, as blockchains have been upgraded from passive distributed ledgers into programmable state machines (thanks to smart contracts), the scope of blockchain interoperability goes beyond just token exchange. In this paper, we present HyperService, the first platform that delivers interoperability and programmability across heterogeneous blockchains. HyperService is powered by two innovative designs: (i) a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and (ii) a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in about 35,000 lines of code to demonstrate its practicality. Our experiment results show that HyperService imposes reasonable latency, in order of seconds, on the end-to-end execution of cross-chain applications

cs.CR↗
arXiv2019

Data Capsule: A New Paradigm for Automatic Compliance with Data Privacy Regulations

The increasing pace of data collection has led to increasing awareness of privacy risks, resulting in new data privacy regulations like General data Protection Regulation (GDPR). Such regulations are an important step, but automatic compliance checking is challenging. In this work, we present a new paradigm, Data Capsule, for automatic compliance checking of data privacy regulations in heterogeneous data processing infrastructures. Our key insight is to pair up a data subject's data with a policy governing how the data is processed. Specified in our formal policy language: PrivPolicy, the policy is created and provided by the data subject alongside the data, and is associated with the data throughout the life-cycle of data processing (e.g., data transformation by data processing systems, data aggregation of multiple data subjects' data). We introduce a solution for static enforcement of privacy policies based on the concept of residual policies, and present a novel algorithm based on abstract interpretation for deriving residual policies in PrivPolicy. Our solution ensures compliance automatically, and is designed for deployment alongside existing infrastructure. We also design and develop PrivGuard, a reference data capsule manager that implements all the functionalities of Data Capsule paradigm.

cs.CY↗
arXiv2019

Research on Autonomous Maneuvering Decision of UCAV based on Approximate Dynamic Programming

Unmanned aircraft systems can perform some more dangerous and difficult missions than manned aircraft systems. In some highly complicated and changeable tasks, such as air combat, the maneuvering decision mechanism is required to sense the combat situation accurately and make the optimal strategy in real-time. This paper presents a formulation of a 3-D one-on-one air combat maneuvering problem and an approximate dynamic programming approach for computing an optimal policy on autonomous maneuvering decision making. The aircraft learns combat strategies in a Reinforcement Leaning method, while sensing the environment, taking available maneuvering actions and getting feedback reward signals. To solve the problem of dimensional explosion in the air combat, the proposed method is implemented through feature selection, trajectory sampling, function approximation and Bellman backup operation in the air combat simulation environment. This approximate dynamic programming approach provides a fast response to a rapidly changing tactical situation, learns in long-term planning, without any explicitly coded air combat rule base.

cs.RO↗
arXiv2019

Moments and One level density of quadratic Hecke $L$-functions of $\mathbb{Q}(ω)$

In this paper, we evaluate explicitly certain quadratic Hecke Gauss sums of $\mathbb{Q}(ω), ω=\exp \left( \frac {2πi}{3}\right)$. As applications, we study the moments of central values of quadratic Hecke $L$-functions of $\mathbb{Q}(ω)$, and establish quantitative non-vanishing result for the $L$-values. We also establish an one level density result for the low-lying zeros of quadratic Hecke $L$-functions of $\mathbb{Q}(ω)$.

math.NT↗
arXiv2019

The mean square of real character sums

In this paper, we evaluate a smoothed sum of the form $\displaystyle \sideset{}{^*}\sum_{d \leq X}\left(\sum_{n \leq Y} \left(\frac{8d}{n} \right ) \right)^2$, where $\left ( \frac{8d}{\cdot} \right )$ is the Kronecker symbol and $\sideset{}{^*}\sum$ denotes a sum over positive odd square-free integers.

math.NT↗
arXiv2019

Multi-modality Latent Interaction Network for Visual Question Answering

Exploiting relationships between visual regions and question words have achieved great success in learning multi-modality features for Visual Question Answering (VQA). However, we argue that existing methods mostly model relations between individual visual regions and words, which are not enough to correctly answer the question. From humans' perspective, answering a visual question requires understanding the summarizations of visual and language information. In this paper, we proposed the Multi-modality Latent Interaction module (MLI) to tackle this problem. The proposed module learns the cross-modality relationships between latent visual and language summarizations, which summarize visual regions and question into a small number of latent representations to avoid modeling uninformative individual region-word relations. The cross-modality information between the latent summarizations are propagated to fuse valuable information from both modalities and are used to update the visual and word features. Such MLI modules can be stacked for several stages to model complex and latent relations between the two modalities and achieves highly competitive performance on public VQA benchmarks, VQA v2.0 and TDIUC . In addition, we show that the performance of our methods could be significantly improved by combining with pre-trained language model BERT.

cs.CV↗
arXiv2019

Joint Multi-frame Detection and Segmentation for Multi-cell Tracking

Tracking living cells in video sequence is difficult, because of cell morphology and high similarities between cells. Tracking-by-detection methods are widely used in multi-cell tracking. We perform multi-cell tracking based on the cell centroid detection, and the performance of the detector has high impact on tracking performance. In this paper, UNet is utilized to extract inter-frame and intra-frame spatio-temporal information of cells. Detection performance of cells in mitotic phase is improved by multi-frame input. Good detection results facilitate multi-cell tracking. A mitosis detection algorithm is proposed to detect cell mitosis and the cell lineage is built up. Another UNet is utilized to acquire primary segmentation. Jointly using detection and primary segmentation, cells can be fine segmented in highly dense cell population. Experiments are conducted to evaluate the effectiveness of our method, and results show its state-of-the-art performance.

cs.CV↗
arXiv2019

Interface ferromagnetism and anomalous Hall effect of CdO/ferromagnetic insulator heterostructures

The experimental observation of quantum anomalous Hall effect (QAHE) in magnetic topological insulators has stimulated enormous interest in condensed-matter physics and materials science. For the purpose of realizing high-temperature QAHE, several material candidates have been proposed, among which the interface states in the CdO/ferromagnetic insulator heterostructures are particularly interesting and favorable for technological applications. Here, we report the experimental observation of the interfacial ferromagnetism and anomalous Hall effect in the Fe3O4/CdO/Fe3O4 heterostructures grown via oxide molecular-beam epitaxy. Systematical variation of the CdO thickness reveals the interface ferromagnetism as the major cause for the observed planar magnetoresistance and anomalous Hall effect. Our results might pave the way to engineer oxide interface states for the exploration of QAHE towards exotic quantum-physical phenomena and potential applications.

cond-mat.mtrl-sci↗
arXiv2019

FPGA-based Binocular Image Feature Extraction and Matching System

Image feature extraction and matching is a fundamental but computation intensive task in machine vision. This paper proposes a novel FPGA-based embedded system to accelerate feature extraction and matching. It implements SURF feature point detection and BRIEF feature descriptor construction and matching. For binocular stereo vision, feature matching includes both tracking matching and stereo matching, which simultaneously provide feature point correspondences and parallax information. Our system is evaluated on a ZYNQ XC7Z045 FPGA. The result demonstrates that it can process binocular video data at a high frame rate (640$\times$480 @ 162fps). Moreover, an extensive test proves our system has robustness for image compression, blurring and illumination.

cs.CV↗