arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 541 records · Page 30Linked to original sources

arXiv2020

Statistical properties of eigenvalues of Laplace-Beltrami operators

We study the eigenvalues of a Laplace-Beltrami operator defined on the set of the symmetric polynomials, where the eigenvalues are expressed in terms of partitions of integers. By assigning partitions with the restricted uniform measure, the restricted Jack measure, the uniform measure or the Plancherel measure, we prove that the global distribution of the eigenvalues is asymptotically a new distribution $μ$, the Gamma distribution, the Gumbel distribution and the Tracy-Widom distribution, respectively. An explicit representation of $μ$ is obtained by a function of independent random variables. We also derive an independent result on random partitions itself: a law of large numbers for the restricted uniform measure. Two open problems are also asked.

math.PR↗

arXiv2020

Infall in massive clumps harboring bright infrared sources

Thirty massive clumps associated with bright infrared sources were observed to detect the infall signatures and characterize infall properties in the envelope of the massive clumps by APEX telescope in CO(4-3) and C$^{17}$O(3-2) lines. Eighteen objects have "blue profile" in CO(4-3) line with virial parameters less than 2, suggesting that global collapse is taking place in these massive clumps. The CO(4-3) lines were fitted by the two-layer model in order to obtain infall velocities and mass infall rates. Derived mass infall rates are from 10$^{-3}$ to 10$^{-1}$ M$_{\odot}$yr$^{-1}$. A positive relationship between clump mass and infall rate appears to indicate that gravity plays a dominant role in the collapsing process. Higher luminosity clump has larger mass infall rate, implying that the clump with higher mass infall rate has higher star formation rate.

astro-ph.GA↗

arXiv2020

Reduce Transmission Delay for Cache-Aided Two-Layer Network

In this paper, we consider a cache-aided relay network, where a single server consisting of a library of N files connects with K1 relays through a shared noiseless link, and each relay connects with K2 users through a shared noiseless link. Each relay and user are equipped with a cache memory of M1 and M2 files, respectively. We propose a centralized and a decentralized coded caching scheme that exploit the spared transmission time resource by allowing concurrent transmission between the two layers. It is shown that both caching schemes are approximately optimal, and greatly reduce the transmission delay compared to the previously known caching schemes. Surprisingly, we show that when the relay's caching size is equal to a threshold that is strictly smaller than N (e.g. M1=0.382N under the decentralized setup and (K1-1)N/K1 under the centralized setup, when K1=2), our schemes achieve the same delay as if each relay had access to the full library. To our best knowledge, this is the first result showing that even the caching size is strictly smaller than the library's size, increasing the caching size is wasteful in reducing the transmission latency.

cs.IT↗

arXiv2020

Capping layer influence and isotropic in-plane upper critical field of the superconductivity at the FeSe/SrTiO3 interface

Understanding the superconductivity at the interface of FeSe/SrTiO3 is a problem of great contemporary interest due to the significant increase in critical temperature (Tc) compared to that of bulk FeSe, as well as the possibility of an unconventional pairing mechanism and topological superconductivity. We report a study of the influence of a capping layer on superconductivity in thin films of FeSe grown on SrTiO3 using molecular beam epitaxy. We used in vacuo four-probe electrical resistance measurements and ex situ magneto-transport measurements to examine the effect of three capping layers that provide distinctly different charge transfer into FeSe: compound FeTe, non-metallic Te, and metallic Zr. Our results show that FeTe provides an optimal cap that barely influences the inherent Tc found in pristine FeSe/SrTiO3, while the transfer of holes from a non-metallic Te cap completely suppresses superconductivity and leads to insulating behavior. Finally, we used ex situ magnetoresistance measurements in FeTe-capped FeSe films to extract the angular dependence of the in-plane upper critical magnetic field. Our observations reveal an almost isotropic in-plane upper critical field, providing insight into the symmetry and pairing mechanism of high temperature superconductivity in FeSe.

cond-mat.supr-con↗

arXiv2020

A Thermochemical Database from High-throughput First-Principles Calculations and Its Application to Analyzing Phase Evolution in AM-fabricated IN718

A comprehensive thermochemical database is constructed based on high-throughput first-principles phonon calculations of over 3000 atomic structures in Ni, Fe, and Co alloys involving a total of 26 elements including Al, B, C, Cr, Cu, Hf, La, Mn, Mo, N, Nb, O, P, Re, Ru, S, Si, Ta, Ti, V, W, Y, and Zr, providing thermochemical data largely unavailable from existing experiments. The database can be employed to predict the equilibrium phase compositions and fractions at a given temperature and an overall chemical composition directly from first-principles by minimizing the chemical potential. It is applied to the additively manufactured nickel-based IN718 superalloy to analyze the phase evolution with temperature. In particular, we successfully predicted the formation of L1$_0$-FeNi, $γ'$-Ni$_3$(Fe,Al), $α$-Cr, $γ$-Ni$_3$(Nb,Mo), $γ''$-Ni$_3$Nb , and $η$-Ni$_3$Ti at low temperatures, $γ'$-Ni$_3$Al, $δ$-Ni$_3$Nb, $γ''$-Ni$_3$Nb, $α$-Cr, and $γ$-Ni(Fe,Cr,Mo) at intermediate temperatures, and $δ$-Ni$_3$Nb and $γ$-Ni(Fe,Cr,Mo) at high temperatures in IN718. These predictions are validated by EDS mapping of compositional distributions and corresponding identifications of phase distributions. The database is expected to be a valuable source for future thermodynamic analysis and microstructure prediction of alloys involving the 26 elements.

cond-mat.mtrl-sci↗

arXiv2020

Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing

With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine translation systems and human translators needs to be manually closed by post-editing. In this paper, we propose an end-to-end deep learning framework of the quality estimation and automatic post-editing of the machine translation output. Our goal is to provide error correction suggestions and to further relieve the burden of human translators through an interpretable model. To imitate the behavior of human translators, we design three efficient delegation modules -- quality estimation, generative post-editing, and atomic operation post-editing and construct a hierarchical model based on them. We examine this approach with the English--German dataset from WMT 2017 APE shared task and our experimental results can achieve the state-of-the-art performance. We also verify that the certified translators can significantly expedite their post-editing processing with our model in human evaluation.

cs.CL↗

arXiv2020

Tuning Chern Number in Quantum Anomalous Hall Insulators

The quantum anomalous Hall (QAH) state is a two-dimensional topological insulating state that has quantized Hall resistance of h/Ce2 and vanishing longitudinal resistance under zero magnetic field, where C is called the Chern number. The QAH effect has been realized in magnetic topological insulators (TIs) and magic-angle twisted bilayer graphene. Despite considerable experimental efforts, the zero magnetic field QAH effect has so far been realized only for C = 1. Here we used molecular beam epitaxy to fabricate magnetic TI multilayers and realized the QAH effect with tunable Chern number C up to 5. The Chern number of these QAH insulators is tuned by varying the magnetic doping concentration or the thickness of the interior magnetic TI layers in the multilayer samples. A theoretical model is developed to understand our experimental observations and establish phase diagrams for QAH insulators with tunable Chern numbers. The realization of QAH insulators with high tunable Chern numbers facilitates the potential applications of dissipationless chiral edge currents in energy-efficient electronic devices and opens opportunities for developing multi-channel quantum computing and higher-capacity chiral circuit interconnects.

cond-mat.mes-hall↗

arXiv2020

Parameterized tests of general relativity with gravitational wave generation and propagation

Any modification on gravity would affect not only gravitational wave (GW) generation but also GW propagation. Therefore, tests of general relativity (GR) with only GW generation or GW propagation will lead to an overestimate for deviations. Here we try to use one set of parameters to parameterize the modifications on both GW generation and GW propagation and then test GR with GW150914. In our simplest case, we find that graviton mass $μ<6.3\times10^{-23}{\rm eV/c^2}$ at $90\%$ C.L. and there are no deviations from GR at $90\%$ C.L..

gr-qc↗

arXiv2020

Magnon-magnon interaction and magnon relaxation time in ferromagnetic Cr2Ge2Te6 monolayer

Despite the intense amount of attention and huge potential of two-dimensional (2D) magnets for applications in novel magnetic, magneto-optical, magneto-thermal and magneto-electronic devices, there has yet to be a robust strategy developed to systematically understand magnon-magnon (MMI) interactions at finite temperature. In this paper, we present a first-principles theoretical method to introduce the finite temperature magnon-magnon interaction into Heisenberg Hamiltonian through a nonlinear correction energy. The Wick theorem is used to decouple the four-magnon operators to two-magnon order. We demonstrate the capabilities of this method by studying the strength of MMI in Cr2Ge2Te6 (CGT) monolayer. The spin wave spectrum at finite temperature and the time-dependent spin autocorrelation function are explored. It is found that the magnon relaxation time due to magnon-magnon scattering increases with temperature because of the reduction in magnon energy, while decreases with wavevector and external magnetic field. Our results provide a new insight to understand the magnon damping and energy dissipation in two-dimensional ferromagnetic materials.

cond-mat.mes-hall↗

arXiv2020

Dipole coupling of a tunable hole double quantum dot in germanium hut wire to a microwave resonator

The germanium (Ge) hut wire system has strong spin-orbit coupling, a long coherence time due to a very large heavy-light hole splitting, and the advantage of site-controlled large-scale hut wire positioning. These properties make the Ge hut wire a promising candidate for the realization of strong coupling of spin to superconducting resonators and scalability for multiple qubit coupling. We have coupled a reflection line resonator to a hole double quantum dot (DQD) formed in Ge hut wire. The amplitude and phase responses of the microwave resonator revealed that the charge stability diagrams of the DQD are in good agreement with those obtained from transport measurements. The DQD interdot tunneling rate is shown to be tunable from 6.2 GHz to 8.5 GHz, which demonstrates the ability to adjust the frequency detuning between the qubit and the resonator. Furthermore, we achieved a hole-resonator coupling strength of up to 15 MHz, with a charge qubit decoherence rate of 0.28 GHz. Meanwhile the hole spin-resonator coupling rate was estimated to be 3 MHz. These results suggest that holes of a DQD in a Ge hut wire are dipole coupled to microwave photons, potentially enabling tunable hole spin-photon interactions in Ge with an inherent spin-orbit coupling.

cond-mat.mes-hall↗

arXiv2020

Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas

Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which is becoming increasingly mature and accurate, but it tends to be fragile under challenging environments. Comparing with classical geometry-based methods, deep learning-based methods can automatically learn effective and robust representations, such as depth, optical flow, feature, ego-motion, etc., from data without explicit computation. Nevertheless, there still lacks a thorough review of the recent advances of deep learning-based VO (Deep VO). Therefore, this paper aims to gain a deep insight on how deep learning can profit and optimize the VO systems. We first screen out a number of qualifications including accuracy, efficiency, scalability, dynamicity, practicability, and extensibility, and employ them as the criteria. Then, using the offered criteria as the uniform measurements, we detailedly evaluate and discuss how deep learning improves the performance of VO from the aspects of depth estimation, feature extraction and matching, pose estimation. We also summarize the complicated and emerging areas of Deep VO, such as mobile robots, medical robots, augmented reality and virtual reality, etc. Through the literature decomposition, analysis, and comparison, we finally put forward a number of open issues and raise some future research directions in this field.

cs.CV↗

arXiv2020

Statistical inference for principal components of spiked covariance matrices

In this paper, we study the asymptotic behavior of the extreme eigenvalues and eigenvectors of the high dimensional spiked sample covariance matrices, in the supercritical case when a reliable detection of spikes is possible. Especially, we derive the joint distribution of the extreme eigenvalues and the generalized components of the associated eigenvectors, i.e., the projections of the eigenvectors onto arbitrary given direction, assuming that the dimension and sample size are comparably large. In general, the joint distribution is given in terms of linear combinations of finitely many Gaussian and Chi-square variables, with parameters depending on the projection direction and the spikes. Our assumption on the spikes is fully general. First, the strengths of spikes are only required to be slightly above the critical threshold and no upper bound on the strengths is needed. Second, multiple spikes, i.e., spikes with the same strength, are allowed. Third, no structural assumption is imposed on the spikes. Thanks to the general setting, we can then apply the results to various high dimensional statistical hypothesis testing problems involving both the eigenvalues and eigenvectors. Specifically, we propose accurate and powerful statistics to conduct hypothesis testing on the principal components. These statistics are data-dependent and adaptive to the underlying true spikes. Numerical simulations also confirm the accuracy and powerfulness of our proposed statistics and illustrate significantly better performance compared to the existing methods in the literature. Especially, our methods are accurate and powerful even when either the spikes are small or the dimension is large.

math.ST↗

arXiv2020

Scanning Transmission Electron Tomography and Electron Energy Loss Spectroscopy of Silicon Metalattices

Transmission electron microscopy, scanning transmission electron tomography, and electron energy loss spectroscopy were used to characterize three-dimensional artificial Si nanostructures called "metalattices", focusing on Si metalattices synthesized by high-pressure confined chemical vapor deposition in 30-nm colloidal silica templates with ~7 and ~12 nm "meta-atoms" and ~2 nm "meta-bonds". The "meta-atoms" closely replicate the shape of the tetrahedral and octahedral interstitial sites of the face-entered cubic colloidal silica template. Composed of either amorphous or nanocrystalline silicon, the metalattice exhibits long-range order and interconnectivity in two-dimensional micrographs and three-dimensional reconstructions. Electron energy loss spectroscopy provides information on local electronic structure. The Si L2,3 core-loss edge is blue-shifted compared to the onset for bulk Si, with the meta-bonds displaying a larger shift (0.55 eV) than the two types of meta-atoms (0.30 and 0.17 eV). Local density of state calculations using an empirical tight binding method are in reasonable agreement.

cond-mat.mtrl-sci↗

arXiv2020

Principal components of spiked covariance matrices in the supercritical regime

In this paper, we study the asymptotic behavior of the extreme eigenvalues and eigenvectors of the spiked covariance matrices, in the supercritical regime. Specifically, we derive the joint distribution of the extreme eigenvalues and the generalized components of their associated eigenvectors in this regime.

math.ST↗

arXiv2020

Revisiting Adversarially Learned Injection Attacks Against Recommender Systems

Recommender systems play an important role in modern information and e-commerce applications. While increasing research is dedicated to improving the relevance and diversity of the recommendations, the potential risks of state-of-the-art recommendation models are under-explored, that is, these models could be subject to attacks from malicious third parties, through injecting fake user interactions to achieve their purposes. This paper revisits the adversarially-learned injection attack problem, where the injected fake user `behaviors' are learned locally by the attackers with their own model -- one that is potentially different from the model under attack, but shares similar properties to allow attack transfer. We found that most existing works in literature suffer from two major limitations: (1) they do not solve the optimization problem precisely, making the attack less harmful than it could be, (2) they assume perfect knowledge for the attack, causing the lack of understanding for realistic attack capabilities. We demonstrate that the exact solution for generating fake users as an optimization problem could lead to a much larger impact. Our experiments on a real-world dataset reveal important properties of the attack, including attack transferability and its limitations. These findings can inspire useful defensive methods against this possible existing attack.

cs.LG↗

arXiv2020

Impact of 150keV and 590keV proton irradiation on monolayer MoS2

We present a comprehensive study on the effects of proton irradiation at different energies (150 and 590 keV) with the fluence of 1x 1012 proton/cm2 on monolayer MoS2. This study not only improves our understanding of the influence of high-energy proton beams on MoS2 but also has implications for radiation-induced changes in device processing and engineering of devices from multilayer MoS2 starting material. Increasing defect density with decreasing proton irradiation energy was observed from photoluminescence spectroscopy study. These defects are attributed to sulfur vacancies observed through x-ray photoelectron spectroscopy analysis and confirmed by transmission electron microscope imaging. Scanning electron microscopy images showed the creation of grain boundaries after proton irradiation. A higher degree of surface deformation was detected with lower irradiation energies through atomic force microscopy. Inter-defect distance is increased with the increase in proton energy irradiation as estimated by transmission electron microscopy imaging. Raman spectroscopy reveals negligible structural changes in the crystal quality after the irradiation. These deformation damages due to proton irradiation are insignificant at the MoS2 layer. Based on the overall influence of low energy proton irradiation on the material characteristics, ML-MoS2 materials can be considered robust and reliable building blocks for 2D material based devices for space applications.

physics.app-ph↗

arXiv2020

A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation

Cooperative overtaking is believed to have the capability of improving road safety and traffic efficiency by means of the real-time information exchange between traffic participants, including road infrastructures, nearby vehicles and others. In this paper, we focused on the critical issues of modeling, computation, and analysis of cooperative overtaking and made it playing a key role in the road overtaking area. In detail, for the purpose of extending the awareness of the surrounding environment, the lane markings in front of ego vehicle were detected and modeled with Bezier curve using an onboard camera. While the nearby vehicle positions were obtained through the vehicle-to-vehicle communication scheme making assure of the accuracy of localization. Then, Gaussian-based conflict potential field was proposed to guarantee the overtaking safety, which can quantitatively estimate the oncoming collision danger. To support the proposed method, many experiments were conducted on the human-in-the-loop simulation platform. The results demonstrated that our proposed method achieves better performance, especially in some unpredictable nature road circumstances.

eess.SP↗

arXiv2020

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. Methods: The task of the challenge was to reconstruct radially acquired multi-coil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, e.g., density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient meta-data accompanying open data sets. Defining reproducibility quantitatively turned out to be non-trivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.

eess.IV↗