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Peng Chen

Publications and source records attributed to Peng Chen.

At least 397 records · Page 22Linked to original sources
arXiv2019

Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches

The model-based power allocation algorithm has been investigated for decades, but it requires the mathematical models to be analytically tractable and it usually has high computational complexity. Recently, the data-driven model-free machine learning enabled approaches are being rapidly developed to obtain near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as of great potential for future intelligent networks. In this paper, the DRL approaches are considered for power control in multi-user wireless communication cellular networks. Considering the cross-cell cooperation, the off-line/on-line centralized training and the distributed execution, we present a mathematical analysis for the DRL-based top-level design. The concrete DRL design is further developed based on this foundation, and policy-based REINFORCE, value-based deep Q learning (DQL), actor-critic deep deterministic policy gradient (DDPG) algorithms are proposed. Simulation results show that the proposed data-driven approaches outperform the state-of-art model-based methods on sum-rate performance, with good generalization power and faster processing speed. Furthermore, the proposed DDPG outperforms the REINFORCE and DQL in terms of both sum-rate performance and robustness, and can be incorporated into existing resource allocation schemes due to its generality.

cs.IT↗
arXiv2018

CP Symmetries as Guiding Posts: Revamping Tri-Bi-Maximal Mixing-I

We analyze the possible generalized CP symmetries admitted by the Tri-Bi-Maximal (TBM) neutrino mixing. Taking advantage of these symmetries we construct in a systematic way other variants of the standard TBM ansatz. Depending on the type and number of generalized CP symmetries imposed, we get new mixing matrices, all of which related to the original TBM matrix. One of such "revamped" TBM variants is the recently discussed mixing matrix of arXiv:1806.03367. We also briefly discuss the phenomenological implications following from these mixing patterns.

hep-ph↗
arXiv2018

Sparse Bayesian Learning-Based Direction Finding Method With Unknown Mutual Coupling Effect

The imperfect array degrades the direction finding performance. In this paper, we investigate the direction finding problem in uniform linear array (ULA) system with unknown mutual coupling effect between antennas. By exploiting the target sparsity in the spatial domain, sparse Bayesian learning (SBL)-based model is proposed and converts the direction finding problem into a sparse reconstruction problem. In the sparse-based model, the \emph{off-grid} errors are introduced by discretizing the direction area into grids. Therefore, an off-grid SBL model with mutual coupling vector is proposed to overcome both the mutual coupling and the off-grid effect. With the distribution assumptions of unknown parameters including the noise variance, the off-grid vector, the received signals and the mutual coupling vector, a novel direction finding method based on SBL with unknown mutual coupling effect named DFSMC is proposed, where an expectation-maximum (EM)-based step is adopted by deriving the estimation expressions for all the unknown parameters theoretically. Simulation results show that the proposed DFSMC method can outperform state-of-the-art direction finding methods significantly in the array system with unknown mutual coupling effect.

eess.SP↗
arXiv2018

Power Allocation in Multi-user Cellular Networks With Deep Q Learning Approach

The model-driven power allocation (PA) algorithms in the wireless cellular networks with interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays, the data-driven model-free machine learning-based approaches are rapidly developed in this field, and among them the deep reinforcement learning (DRL) is proved to be of great promising potential. Different from supervised learning, the DRL takes advantages of exploration and exploitation to maximize the objective function under certain constraints. In our paper, we propose a two-step training framework. First, with the off-line learning in simulated environment, a deep Q network (DQN) is trained with deep Q learning (DQL) algorithm, which is well-designed to be in consistent with this PA issue. Second, the DQN will be further fine-tuned with real data in on-line training procedure. The simulation results show that the proposed DQN achieves the highest averaged sum-rate, comparing to the ones with present DQL training. With different user densities, our DQN outperforms benchmark algorithms and thus a good generalization ability is verified.

cs.IT↗
arXiv2018

Evolution of orbital angular momentum in a soft quasi-periodic structure with topological defects

We propose a quasi-periodic structure (QPS) with topological defects. The analytical expression of the corresponding Fourier spectrum is derived, which reflects the combined effects of topological structure and quasitranslational symmetry. Light-matter interaction therein brings unusual diffraction characteristics with exotic evolution of orbital angular momentum (OAM). Long-range correlation of QPS resulted in multi-fractal and pairwise distribution of optical singularities. A general conversation law of OAM was revealed. A liquid crystal photopatterning QPS is fabricated to demonstrate the above characteristics. Dynamic reconfigurable manipulation of optical singularities was achieved. Our approach offers the opportunity to manipulate OAM with multiple degrees of freedom, which has promising applications in multi-channel quantum information processing and highdimensional quantum state generation.

physics.optics↗
arXiv2018

Measurement of the strontium triplet Rydberg series by depletion spectroscopy of ultracold atoms

We report on the atom loss spectroscopy of strontium Rydberg atoms in a magneto-optical trap, using a two-photon excitation scheme through the intermediate state $5\mathrm{s}5\mathrm{p} \, ^3\mathrm{P}_1$. Energies of the $5\mathrm{s}n\mathrm{s} \, ^3\mathrm{S}_1$ and $5\mathrm{s}n\mathrm{d} \, ^3\mathrm{D}_{1,2}$ Rydberg series of $^{88}$Sr in the range $13 \leq n \leq 50$ are determined with an absolute accuracy of 10 MHz, including the perturbed region where the $5\mathrm{s}n\mathrm{d} \, ^3\mathrm{D}_{2}$ series couples to the $5\mathrm{s}n\mathrm{d} \, ^1\mathrm{D}_{2}$ series. This represents an improvement by more than two orders of magnitude compared to previously published data. The quantum defects for each series are determined using the extended Rydberg-Ritz formula in the range where there is no strong perturbation. A value of $1\,377\,012\,721(10) \,\mathrm{MHz}$ for the first ionization limit of $^{88}$Sr is extracted.

physics.atom-ph↗
arXiv2018

Limit theorems for the tagged particle in exclusion processes on regular trees

We consider exclusion processes on a rooted $d$-regular tree. We start from a Bernoulli product measure conditioned on having a particle at the root, which we call the tagged particle. For $d\geq 3$, we show that the tagged particle has positive linear speed and satisfies a central limit theorem. We give an explicit formula for the speed. As a key step in the proof, we first show that the exclusion process "seen from the tagged particle" has an ergodic invariant measure.

math.PR↗
arXiv2018

Non-Hermitian magnon-photon interference in an atomic ensemble

The beam-splitter (BS) is one of the most common and important components in modern optics, and lossless BS which features unitary transformation induces Hermitian evolution of light. However, the practical BS based on the conversion between different degree of freedoms are naturally non-Hermitian, as a result of essentially open quantum dynamics. In this work, we experimentally demonstrate a non-Hermitian BS for the interference between traveling photonic and localized magnonic modes. The non-Hermitian magnon-photon BS is achieved by the coherent and incoherent interaction mediated by the excited levels of atoms, which is reconfigurable by adjusting the detuning of excitation. Unconventional correlated interference pattern is observed at the photon and magnon output ports. Our work is potential for extending to single-quantum level to realize interference between a single photon and magnon, which provides an efficient and simple platform for future tests of non-Hermitian quantum physics.

quant-ph↗
arXiv2018

Sparse polynomial approximations for affine parametric saddle point problems

In this work we study convergence properties of sparse polynomial approximations for a class of affine parametric saddle point problems. Such problems can be found in many computational science and engineering fields, including the Stokes equations for viscous incompressible flow, mixed formulation of diffusion equations for heat conduction or groundwater flow, time-harmonic Maxwell equations for electromagnetics, etc. Due to the lack of knowledge or intrinsic randomness, the coefficients of such problems are uncertain and can often be represented or approximated by high- or countably infinite-dimensional random parameters equipped with suitable probability distributions, and the coefficients affinely depend on a series of either globally or locally supported basis functions, e.g., Karhunen--Loève expansion, piecewise polynomials, or adaptive wavelet approximations. Consequently, we are faced with solving affine parametric saddle point problems. Here we study sparse polynomial approximations of the parametric solutions, in particular sparse Taylor approximations, and their convergence properties for these parametric problems. With suitable sparsity assumptions on the parametrization, we obtain the algebraic convergence rates $O(N^{-r})$ for the sparse polynomial approximations of the parametric solutions, in cases of both globally and locally supported basis functions. We prove that $r$ depends only on a sparsity parameter in the parametrization of the random input, and in particular does not depend on the number of active parameter dimensions or the number of polynomial terms $N$. These results imply that sparse polynomial approximations can effectively break the curse of dimensionality, thereby establishing a theoretical foundation for the development and application of such practical algorithms as adaptive, least-squares, and compressive sensing constructions.

math.NA↗
arXiv2018

Hessian-based sampling for high-dimensional model reduction

In this work we develop a Hessian-based sampling method for the construction of goal-oriented reduced order models with high-dimensional parameter inputs. Model reduction is known very challenging for high-dimensional parametric problems whose solutions also live in high-dimensional manifolds. However, the manifold of some quantity of interest (QoI) depending on the parametric solutions may be low-dimensional. We use the Hessian of the QoI with respect to the parameter to detect this low-dimensionality, and draw training samples by projecting the high-dimensional parameter to a low-dimensional subspace spanned by the eigenvectors of the Hessian corresponding to its dominating eigenvalues. Instead of forming the full Hessian, which is computationally intractable for a high-dimensional parameter, we employ a randomized algorithm to efficiently compute the dominating eigenpairs of the Hessian whose cost does not depend on the nominal dimension of the parameter but only on the intrinsic dimension of the QoI. We demonstrate that the Hessian-based sampling leads to much smaller errors of the reduced basis approximation for the QoI compared to a random sampling for a diffusion equation with random input obeying either uniform or Gaussian distributions.

math.NA↗
arXiv2018

Stein's method for asymmetric $α$-stable distributions, with application to the stable CLT

This paper is concerned with the Stein's method associated with a (possibly) asymmetric $α$-stable distribution $Z$, in dimension one. More precisely, its goal is twofold. In the first part, we exhibit a genuine bound for the Wasserstein distance between $Z$ and any integrable random variable $X$, in terms of an operator that reduces to the classical fractional Laplacian in the symmetric case. Then, in the second part we apply the aforementioned bound to compute error rates in the stable central limit theorem, when the entries are in the domain $\mathcal{D}_α$ of normal attraction of a stable law of exponent $α$. To conclude, we study the specific case where the entries are Pareto like multiplied by a slowly varying function, which provides an example of random variables that do not belong to $\mathcal{D}_α$, but for which our approach continues to apply.

math.PR↗
arXiv2018

Question Generation from SQL Queries Improves Neural Semantic Parsing

We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.

cs.CL↗
arXiv2018

Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty

In this work we develop a scalable computational framework for the solution of PDE-constrained optimal control under high-dimensional uncertainty. Specifically, we consider a mean-variance formulation of the control objective and employ a Taylor expansion with respect to the uncertain parameter either to directly approximate the control objective or as a control variate for variance reduction. The expressions for the mean and variance of the Taylor approximation are known analytically, although their evaluation requires efficient computation of the trace of the (preconditioned) Hessian of the control objective. We propose to estimate this trace by solving a generalized eigenvalue problem using a randomized algorithm that only requires the action of the Hessian on a small number of random directions. Then, the computational work does not depend on the nominal dimension of the uncertain parameter, but depends only on the effective dimension, thus ensuring scalability to high-dimensional problems. Moreover, to increase the estimation accuracy of the mean and variance of the control objective by the Taylor approximation, we use it as a control variate for variance reduction, which results in considerable computational savings (several orders of magnitude) compared to a plain Monte Carlo method. We demonstrate the accuracy, efficiency, and scalability of the proposed computational method for two examples with high-dimensional uncertain parameters: subsurface flow in a porous medium modeled as an elliptic PDE, and turbulent jet flow modeled by the Reynolds-averaged Navier--Stokes equations coupled with a nonlinear advection-diffusion equation characterizing model uncertainty. In particular, for the latter more challenging example we show scalability of our algorithm up to one million parameters resulting from discretization of the uncertain parameter field.

math.OC↗
arXiv2018

Style Transfer as Unsupervised Machine Translation

Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content. One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is in one style and the target sentence in another style. With this constraint, in this paper, we adapt unsupervised machine translation methods for the task of automatic style transfer. We first take advantage of style-preference information and word embedding similarity to produce pseudo-parallel data with a statistical machine translation (SMT) framework. Then the iterative back-translation approach is employed to jointly train two neural machine translation (NMT) based transfer systems. To control the noise generated during joint training, a style classifier is introduced to guarantee the accuracy of style transfer and penalize bad candidates in the generated pseudo data. Experiments on benchmark datasets show that our proposed method outperforms previous state-of-the-art models in terms of both accuracy of style transfer and quality of input-output correspondence.

cs.CL↗
arXiv2018

Neutrino Predictions from Generalized CP Symmetries of Charged Leptons

We study the implications of generalized CP transformations acting on the mass matrices of charged leptons in a model-independent way. Generalized $e-μ$, $e-τ$ and $μ-τ$ symmetries are considered in detail. In all cases the physical parameters of the lepton mixing matrix, three mixing angles and three CP phases can be expressed in terms of a restricted set of independent "theory" parameters that characterize a given choice of CP transformation. This leads to implications for neutrino oscillations as well as neutrinoless double beta decay experiments.

hep-ph↗
arXiv2018

Tomography of the temporal-spectral state of subnatural-linewidth single photons from atomic ensembles

Subnatural-linewidth single-photon source is a potential candidate for exploring the time degree of freedom in photonic quantum information science. This type of single-photon source has been demonstrated to be generated and reshaped in atomic ensembles without any external cavity or filter, and is typically characterized through photon-counting technology. However, the full complex temporal mode function(TMF) of the photon source is not able to be revealed from direct photon counting measurement. Here, for the first time, we demonstrate the complete temporal mode of the subnatural-linewidth single photons generated from a cold atomic cloud. Through heterodyne detection between the single photon and a local oscillator with various central frequencies, we recover the temporal density matrix of the single photons at resolvable time bins. Further we demonstrate that the reduced autocorrelation function measured through homodyne detection perfectly reveals the pure temporal-spectral state of the subnatural-linewidth single photons.

quant-ph↗
arXiv2018

In-situ strain tuning of the Dirac surface states in Bi2Se3 films

Elastic strain has the potential for a controlled manipulation of the band gap and spin-polarized Dirac states of topological materials, which can lead to pseudo-magnetic-field effects, helical flat bands and topological phase transitions. However, practical realization of these exotic phenomena is challenging and yet to be achieved. Here, we show that the Dirac surface states of the topological insulator Bi2Se3 can be reversibly tuned by an externally applied elastic strain. Performing in-situ x-ray diffraction and in-situ angle-resolved photoemission spectroscopy measurements during tensile testing of epitaxial Bi2Se3 films bonded onto a flexible substrate, we demonstrate elastic strains of up to 2.1% and quantify the resulting reversible changes in the topological surface state. Our study establishes the functional relationship between the lattice and electronic structures of Bi2Se3 and, more generally, demonstrates a new route toward momentum-resolved mapping of strain-induced band structure changes.

cond-mat.mtrl-sci↗