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Ke Deng

Ke Deng appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.mtrl-sciphysics.class-phcond-mat.mes-hallSocial and Information NetworksMachine LearningMethodologyphysics.soc-phquant-phComputer Visioncond-mat.str-elNetworking and Internet Architecturephysics.data-an

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Published work

21 published item(s)

preprint2026arXiv

Adaptive Kernel Density Estimation with Pre-training

Density estimation in high-dimensional settings is an important and challenging statistical problem.Traditional methods based on kernel smoothing are inefficient in high dimensions due to the difficulties in specifying appropriate location-adaptive kernels. In this work, we introduce pre-training, a key idea behind many cutting-edge AI technologies, to the context of non-parametric density estimation. By establishing a pre-trained neural network that can recommend an appropriate location-adaptive kernel for each sample point, efficient density estimation with adaptive kernels is achieved in high dimensions. A wide range of numerical experiments show that this strategy is highly effective for improving density-estimation accuracy, when the target distribution is close to the distribution family for pre-training. When the target distribution is substantially different from the pre-training distribution family, the benefit from the proposed pre-training strategy may be diluted, but can be reactivated by an additional fine-tuning procedure.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios

Representation learning in recent years has been addressed with self-supervised learning methods. The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions -- cross-view prediction. Augmentation is one of the key components in cross-view self-supervised learning frameworks to learn visual representations. This paper presents ExAgt, a novel method to include expert knowledge for augmenting traffic scenarios, to improve the learnt representations without any human annotation. The expert-guided augmentations are generated in an automated fashion based on the infrastructure, the interactions between the EGO and the traffic participants and an ideal sensor model. The ExAgt method is applied in two state-of-the-art cross-view prediction methods and the representations learnt are tested in downstream tasks like classification and clustering. Results show that the ExAgt method improves representation learning compared to using only standard augmentations and it provides a better representation space stability. The code is available at https://github.com/lab176344/ExAgt.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Identifying Cost-effective Debunkers for Multi-stage Fake News Mitigation Campaigns

Online social networks have become a fertile ground for spreading fake news. Methods to automatically mitigate fake news propagation have been proposed. Some studies focus on selecting top k influential users on social networks as debunkers, but the social influence of debunkers may not translate to wide mitigation information propagation as expected. Other studies assume a given set of debunkers and focus on optimizing intensity for debunkers to publish true news, but as debunkers are fixed, even if with high social influence and/or high intensity to post true news, the true news may not reach users exposed to fake news and therefore mitigation effect may be limited. In this paper, we propose the multi-stage fake news mitigation campaign where debunkers are dynamically selected within budget at each stage. We formulate it as a reinforcement learning problem and propose a greedy algorithm optimized by predicting future states so that the debunkers can be selected in a way that maximizes the overall mitigation effect. We conducted extensive experiments on synthetic and real-world social networks and show that our solution outperforms state-of-the-art baselines in terms of mitigation effect.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Parameter-free quantum hydrodynamic theory for plasmonics: Electron density-dependent damping rate and diffusion coefficient

Plasmonics is a rapid growing field, which has enabled both fundamental science and inventions of various quantum optoelectronic devices. An accurate and efficient method to calculate the optical response of metallic structures with feature size in the nanoscale plays an important role. Quantum hydrodynamic theory (QHT) provides an efficient description of the free-electron gas, where quantum effects of nonlocality and spill-out are taken into account. In this work, we introduce a general QHT that includes diffusion to account for the broadening, which is a key problem in practical applications of surface plasmon. We will introduce a density-dependent diffusion coefficient to give very accurate linewidth. It is a self-consistent method, in which both the ground and excited states are solved by using the same energy functional, with the kinetic energy described by the Thomas-Fermi and von Weizsäcker (vW) formalisms. In addition, our QHT method is stable by introduction of an electron density-dependent damping rate. For sodium nanosphere of various sizes, the plasmon energy and broadening by our QHT method are in excellent agreement with those by density functional theory and Kreibig formula. By applying our QHT method to sodium jellium nanorods, we clearly show that our method enables a parameter-free simulation, i.e. without resorting to any empirical parameter, such as size-dependent damping rate and diffusing coefficient. It is found that there exists a perfect linear relation between the resonance wavelength and aspect radio. The width decreases with increasing aspect ratio and height. The calculations show that our QHT method provides an explicit and unified way to account for size-dependent frequency shifts and broadening of arbitrarily shaped geometries. It is reliable and robust with great predicability, and hence provides a general and efficient platform to study plasmonics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Half-Magnetic Topological Insulator

Topological magnets are a new family of quantum materials providing great potential to realize emergent phenomena, such as quantum anomalous Hall effect and axion-insulator state. Here we present our discovery that stoichiometric ferromagnet MnBi8Te13 with natural heterostructure MnBi2Te4-(Bi2Te3)3 is an unprecedented half-magnetic topological insulator, with the magnetization existing at the MnBi2Te4 surface but not at the opposite surface terminated by triple Bi2Te3 layers. Our angle-resolved photoemission spectroscopy measurements unveil a massive Dirac gap at the MnBi2Te4 surface, and gapless Dirac cone on the other side. Remarkably, the Dirac gap (~28 meV) at MnBi2Te4 surface decreases monotonically with increasing temperature and closes right at the Curie temperature, thereby representing the first smoking-gun spectroscopic evidence of magnetization-induced topological surface gap among all known magnetic topological materials. We further demonstrate theoretically that the half-magnetic topological insulator is desirable to realize the half-quantized surface anomalous Hall effect, which serves as a direct proof of the general concept of axion electrodynamics in condensed matter systems.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Multi-level Training and Bayesian Optimization for Economical Hyperparameter Optimization

Hyperparameters play a critical role in the performances of many machine learning methods. Determining their best settings or Hyperparameter Optimization (HPO) faces difficulties presented by the large number of hyperparameters as well as the excessive training time. In this paper, we develop an effective approach to reducing the total amount of required training time for HPO. In the initialization, the nested Latin hypercube design is used to select hyperparameter configurations for two types of training, which are, respectively, heavy training and light training. We propose a truncated additive Gaussian process model to calibrate approximate performance measurements generated by light training, using accurate performance measurements generated by heavy training. Based on the model, a sequential model-based algorithm is developed to generate the performance profile of the configuration space as well as find optimal ones. Our proposed approach demonstrates competitive performance when applied to optimize synthetic examples, support vector machines, fully connected networks and convolutional neural networks.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Bound state and non-Markovian dynamics of a quantum emitter around a surface plasmonic nanostructure

A bound state between a quantum emitter (QE) and surface plasmon polaritons (SPPs) can be formed, where the QE is partially stabilized in its excited state. We put forward a general approach for calculating the energy level shift at a negative frequency $ω$, which is just the negative of the nonresonant part for the energy level shift at positive frequency $-ω$. We also propose an efficient formalism for obtaining the long-time value of the excited-state population without calculating the eigenfrequency of the bound state or performing a time evolution of the system, in which the probability amplitude for the excited state in the steady limit is equal to one minus the integral of the evolution spectrum over the positive frequency range. With the above two quantities obtained, we show that the non-Markovian decay dynamics in the presence of a bound state can be obtained by the method based on the Green's function expression for the evolution operator. A general criterion for identifying the existence of a bound state is presented. These are numerically demonstrated for a QE located around a nanosphere and in a gap plasmonic nanocavity. These findings are instructive in the fields of coherent light-matter interactions.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

In-plane antiferromagnetic moments in axion topological insulator candidate EuIn$_2$As$_2$

Topological insulator with antiferromagnetic order can serve as an ideal platform for the realization of axion electrodynamics. In this paper, we report a systematic study of the axion topological insulator candidate EuIn$_2$As$_2$. A linear energy dispersion across the Fermi level confirms the existence of the proposed hole-type Fermi pocket. Spin-flop transitions occur with magnetic fields applied within the $ab$-plane while are absent for fields parallel to the $c$-axis. Anisotropic magnetic phase diagrams are observed and the orientation of the ground magnetic moment is found to be within the $ab$-plane. The magnetoresistivity for EuIn$_2$As$_2$ behaves non-monotonic as a function of field strength. It exhibits angular dependent evolving due to field-driven and temperature-driven magnetic states. These results indicate that the magnetic states of EuIn$_2$As$_2$ strongly affect the transport properties as well as the topological nature.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Acoustic one-way mode conversion and transmission by sonic crystal waveguides

We proposed a scheme to achieve one-way acoustic propagation and even odd mode switching in two mutually perpendicular sonic crystal waveguides connected by a resonant cavity. The even mode in the entrance waveguide is able to switch to odd mode in the exit waveguide through a symmetry match between the cavity resonant modes and the waveguide modes. Conversely, the odd mode in the exit waveguide is unable to be converted into the even mode in the entrance waveguide as incident waves and eigenmodes are mismatched in their symmetries at the waveguide exit. This one way mechanism can be applied to design an acoustic diode for acoustic integration devices and can be used as a convertor of the acoustic waveguide modes.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Experimental observation of topological Fermi arcs in type-II Weyl semimetal MoTe2

Weyl semimetal is a new quantum state of matter [1-12] hosting the condensed matter physics counterpart of relativisticWeyl fermion [13] originally introduced in high energy physics. The Weyl semimetal realized in the TaAs class features multiple Fermi arcs arising from topological surface states [10, 11, 14-16] and exhibits novel quantum phenomena, e.g., chiral anomaly induced negative mag-netoresistance [17-19] and possibly emergent supersymmetry [20]. Recently it was proposed theoretically that a new type (type-II) of Weyl fermion [21], which does not have counterpart in high energy physics due to the breaking of Lorentz invariance, can emerge as topologically-protected touching between electron and hole pockets. Here, we report direct spectroscopic evidence of topological Fermi arcs in the predicted type-II Weyl semimetal MoTe2 [22-24]. The topological surface states are confirmed by directly observing the surface states using bulk-and surface-sensitive angle-resolved photoemission spectroscopy (ARPES), and the quasi-particle interference (QPI) pattern between the two putative Fermi arcs in scanning tunneling microscopy (STM). Our work establishes MoTe2 as the first experimental realization of type-II Weyl semimetal, and opens up new opportunities for probing novel phenomena such as exotic magneto-transport [21] in type-II Weyl semimetals.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Raman signatures of inversion symmetry breaking and structural phase transition in type-II Weyl semimetal MoTe2

Transition metal dichalcogenide MoTe$_2$ is an important candidate for realizing the newly predicted type-IIWeyl fermions, for which the breaking of the inversion symmetry is a prerequisite. Here we present direct spectroscopic evidence for the inversion symmetry breaking in the low temperature phase of MoTe$_2$ by systematic Raman experiments and first principles calculations. We identify five lattice vibrational modes which are Raman active only in noncentrosymmetric structure at low temperature. A hysteresis is also observed in the peak intensity of inversion symmetry activated Raman modes, confirming a temperature induced structural phase transition with a concomitant change in the inversion symmetry. Our results provide definitive evidence for the low temperature noncentrosymmetric T$_d$ phase from vibrational spectroscopy, and suggest MoTe$_2$ as an ideal candidate for investigating the temperature induced topological phase transition.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Broadband acoustic trapping of a particle by a soft plate with a periodic deep grating

We investigated the acoustic radiation force (ARF) acting on a cylindrical brass particle near an acoustically soft plate patterned with a periodic deep grating. The existence of a negative ARF by which the particle can be pulled towards the sound source is confirmed. In addition, the bandwidth for negative ARF in this soft-plate system is found to be considerably broader than in the stiff-plate systems typically used in previous studies. It is further demonstrated by field distribution analysis that the negative ARF is caused by the gradient force induced by the gradient vortex velocity field near the surface, which stems from the collective resonance excitation of the antisymmetric coupling of Scholte surface waves in the thin plate. The effects of particle location and size on the ARF were also investigated in detail. The negative ARF has potential use in applications requiring particle manipulation using acoustic waves.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Fast Parameter Estimation in Loss Tomography for Networks of General Topology

As a technique to investigate link-level loss rates of a computer network with low operational cost, loss tomography has received considerable attentions in recent years. A number of parameter estimation methods have been proposed for loss tomography of networks with a tree structure as well as a general topological structure. However, these methods suffer from either high computational cost or insufficient use of information in the data. In this paper, we provide both theoretical results and practical algorithms for parameter estimation in loss tomography. By introducing a group of novel statistics and alternative parameter systems, we find that the likelihood function of the observed data from loss tomography keeps exactly the same mathematical formulation for tree and general topologies, revealing that networks with different topologies share the same mathematical nature for loss tomography. More importantly, we discover that a re-parametrization of the likelihood function belongs to the standard exponential family, which is convex and has a unique mode under regularity conditions. Based on these theoretical results, novel algorithms to find the MLE are developed. Compared to existing methods in the literature, the proposed methods enjoy great computational advantages.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Monolayer charge-neutral graphene on platinum with extremely weak electron-phonon coupling

Epitaxial growth of graphene on transition metal substrates is an important route for obtaining large scale graphene. However, the interaction between graphene and the substrate often leads to multiple orientations, distorted graphene band structure, large doping and strong electron-phonon coupling. Here we report the growth of monolayer graphene with high crystalline quality on Pt(111) substrate by using a very low concentration of an internal carbon source with high annealing temperature. The controlled growth leads to electronically decoupled graphene: it is nearly charge neutral and has extremely weak electron-phonon coupling (coupling strength $λ$ $\approx$ 0.056) as revealed by angle-resolved photoemission spectroscopic measurements. The thermodynamics and kinetics of the carbon diffusion process is investigated by DFT calculation. Such graphene with negligible graphene-substrate interaction provides an important platform for fundamental research as well as device applications when combined with a nondestructive sample transfer technique.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Multi-resolution community detection based on generalized self-loop rescaling strategy

Community detection is of considerable importance for analyzing the structure and function of complex networks. Many real-world networks may possess community structures at multiple scales, and recently, various multi-resolution methods were proposed to identify the community structures at different scales. In this paper, we present a type of multi-resolution methods by using the generalized self-loop rescaling strategy. The self-loop rescaling strategy provides one uniform ansatz for the design of multi-resolution community detection methods. Many quality functions for community detection can be unified in the framework of the self-loop rescaling. The resulting multi-resolution quality functions can be optimized directly using the existing modularity-optimization algorithms. Several derived multi-resolution methods are applied to the analysis of community structures in several synthetic and real-world networks. The results show that these methods can find the pre-defined substructures in synthetic networks and real splits observed in real-world networks. Finally, we give a discussion on the methods themselves and their relationship. We hope that the study in the paper can be helpful for the understanding of the multi-resolution methods and provide useful insight into designing new community detection methods.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Surface-Plasmon-Polariton (SPP)-Like Acoustic Surface Waves on Elastic Metamaterials

We investigate the dispersion properties of the acoustic surface waves on surface of elastic metamaterials. With an analytical approach, we show that unconventional acoustic surface waves, with dispersion behaviors very similar to the electromagnetic surface plasmon polaritons (SPPs) on metal surfaces, can exist on the elastic metamaterials around the frequency at which the elastic Lamé's constants satisfy lambda+mu=0. Two typical elastic metamaterials are exemplified to demonstrate such peculiar acoustic surface waves.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Designable hybrid sonic crystals for transportation and division of acoustic images

Conventional sonic crystal (SC) devices designed for acoustic imaging can focus acoustic waves from an input source into only one image but not multi-images. Furthermore the output position of formed image cannot be designed at will. In this paper, we propose the hybrid SC imaging devices to achieve multi-images from one-source-input along with the designable image-positions. The proposed hybrid devices can image acoustic waves radiated both from point source and Gaussian beam, which different from conventional SC imaging devices that only applies to point source. These multi-functional but still simple and easy-to-fabricate devices are believed to find extensive applications, particularly in ultrasonic photography and compact acoustic imaging.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Loss Tomography from Tree Topologies to General Topologies

Loss tomography has received considerable attention in recent years and a number of estimators based on maximum likelihood (ML) or Bayesian principles have been proposed. Almost all of the estimators are devoted to the tree topology despite the general topology is more common in practice. There has been few likelihood function devoted to the general topology, not to mention the estimator. To overcome this, two sets of sufficient statistics for the tree and general topologies, respectively, are proposed in this paper. Using the statistics, two likelihood functions, one for a topology, are proposed here and subsequently two likelihood equations for the general topology, one is link-based and the other is path-based, are obtained. In addition, a dependence between subtrees in terms of their estimates is identified for the general topology and a divide-and-conquer strategy is proposed to deal with the dependence, which divides a general network into two types of independent trees. Further, two algorithms, one for a type of the independent trees, are proposed to estimate the loss rates of each type.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Splitting of self-collimated beams in two-dimensional sonic crystals

An easy-to-implement scheme to split self-collimated acoustic beams in sonic crystals (SCs) is proposed by introducing line-defects into SCs, by which an incoming self-collimated beam can be split into a 90 degree-bended one and a transmitted one with an arbitrary power ratio by adjusting the value of defect size. An all-angle and wide-band splitting instrument is demonstrated with nearly perfect efficiency (more than 90%) for Gaussian beams at a wide range of incident angles. Splitting effect for a point source as input is also realized, in which two subwavelength images of the source, i.e., a bended one and a transmitted one, are formed by our designed splitting structure. Finally, the proposed one-to-two splitting scheme is generalized to one-to-N (N>2) cases by inserting more rows of line defects into the SC.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Acoustic guiding and subwavelength imaging with sharp bending by sonic crystal

A sharp bending scheme for the self-collimation of acoustic waves is proposed by simply truncating the sonic crystals. An all-angle and wide-band 90°-bending wave guide is demonstrated with nearly perfect transmissions for Gaussian beams at a wide range of incident angles. A 90°-bended imaging for a point source with a subwavelength resolution of 0 0.37λ is also realized by the proposed structure. These results will find applicability in the manipulation of acoustic waves by sonic crystals.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Evolving network models under a dynamic growth rule

Evolving network models under a dynamic growth rule which comprises the addition and deletion of nodes are investigated. By adding a node with a probability $P_a$ or deleting a node with the probability $P_d=1-P_a$ at each time step, where $P_a$ and $P_d$ are determined by the Logistic population equation, topological properties of networks are studied. All the fat-tailed degree distributions observed in real systems are obtained, giving the evidence that the mechanism of addition and deletion can lead to the diversity of degree distribution of real systems. Moreover, it is found that the networks exhibit nonstationary degree distributions, changing from the power-law to the exponential one or from the exponential to the Gaussian one. These results can be expected to shed some light on the formation and evolution of real complex real-world networks.

0 citations0 reviewsNo code linkedOpen access