R. M. Qiu - IEEE Fellow

R. M. Qiu
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R. M. Qiu
IEEE Fellow

Pubs By Year

Pub Categories

Computer Science - Information Theory (15)
Mathematics - Information Theory (15)
Physics - Mesoscopic Systems and Quantum Hall Effect (6)
Statistics - Methodology (5)
Physics - Strongly Correlated Electrons (5)
Mathematics - Geometric Topology (4)
Computer Science - Networking and Internet Architecture (4)
High Energy Physics - Experiment (3)
Physics - Instrumentation and Detectors (3)
Physics - Materials Science (3)
Statistics - Machine Learning (2)
Statistics - Applications (2)
Nuclear Experiment (2)
Computer Science - Learning (2)
Physics - Statistical Mechanics (1)
Computer Science - Artificial Intelligence (1)
Quantum Physics (1)
Physics - Computational Physics (1)
Physics - Medical Physics (1)
Nuclear Theory (1)
Physics - Disordered Systems and Neural Networks (1)

Publications Authored By R. M. Qiu

Authors: F. P. An, A. B. Balantekin, H. R. Band, M. Bishai, S. Blyth, D. Cao, G. F. Cao, J. Cao, Y. L. Chan, J. F. Chang, Y. Chang, H. S. Chen, Q. Y. Chen, S. M. Chen, Y. X. Chen, Y. Chen, J. Cheng, Z. K. Cheng, J. J. Cherwinka, M. C. Chu, A. Chukanov, J. P. Cummings, Y. Y. Ding, M. V. Diwan, M. Dolgareva, J. Dove, D. A. Dwyer, W. R. Edwards, R. Gill, M. Gonchar, G. H. Gong, H. Gong, M. Grassi, W. Q. Gu, L. Guo, X. H. Guo, Y. H. Guo, Z. Guo, R. W. Hackenburg, S. Hans, M. He, K. M. Heeger, Y. K. Heng, A. Higuera, Y. B. Hsiung, B. Z. Hu, T. Hu, E. C. Huang, H. X. Huang, X. T. Huang, Y. B. Huang, P. Huber, W. Huo, G. Hussain, D. E. Jaffe, K. L. Jen, X. P. Ji, X. L. Ji, J. B. Jiao, R. A. Johnson, D. Jones, L. Kang, S. H. Kettell, A. Khan, S. Kohn, M. Kramer, K. K. Kwan, M. W. Kwok, T. J. Langford, K. Lau, L. Lebanowski, J. Lee, J. H. C. Lee, R. T. Lei, R. Leitner, J. K. C. Leung, C. Li, D. J. Li, F. Li, G. S. Li, Q. J. Li, S. Li, S. C. Li, W. D. Li, X. N. Li, X. Q. Li, Y. F. Li, Z. B. Li, H. Liang, C. J. Lin, G. L. Lin, S. Lin, S. K. Lin, Y. -C. Lin, J. J. Ling, J. M. Link, L. Littenberg, B. R. Littlejohn, J. L. Liu, J. C. Liu, C. W. Loh, C. Lu, H. Q. Lu, J. S. Lu, K. B. Luk, X. Y. Ma, X. B. Ma, Y. Q. Ma, Y. Malyshkin, D. A. Martinez Caicedo, K. T. McDonald, R. D. McKeown, I. Mitchell, Y. Nakajima, J. Napolitano, D. Naumov, E. Naumova, H. Y. Ngai, J. P. Ochoa-Ricoux, A. Olshevskiy, H. -R. Pan, J. Park, S. Patton, V. Pec, J. C. Peng, L. Pinsky, C. S. J. Pun, F. Z. Qi, M. Qi, X. Qian, R. M. Qiu, N. Raper, J. Ren, R. Rosero, B. Roskovec, X. C. Ruan, H. Steiner, P. Stoler, J. L. Sun, W. Tang, D. Taychenachev, K. Treskov, K. V. Tsang, C. E. Tull, N. Viaux, B. Viren, V. Vorobel, C. H. Wang, M. Wang, N. Y. Wang, R. G. Wang, W. Wang, X. Wang, Y. F. Wang, Z. Wang, Z. Wang, Z. M. Wang, H. Y. Wei, L. J. Wen, K. Whisnant, C. G. White, L. Whitehead, T. Wise, H. L. H. Wong, S. C. F. Wong, E. Worcester, C. -H. Wu, Q. Wu, W. J. Wu, D. M. Xia, J. K. Xia, Z. Z. Xing, J. L. Xu, Y. Xu, T. Xue, C. G. Yang, H. Yang, L. Yang, M. S. Yang, M. T. Yang, Y. Z. Yang, M. Ye, Z. Ye, M. Yeh, B. L. Young, Z. Y. Yu, S. Zeng, L. Zhan, C. Zhang, C. C. Zhang, H. H. Zhang, J. W. Zhang, Q. M. Zhang, R. Zhang, X. T. Zhang, Y. M. Zhang, Y. X. Zhang, Y. M. Zhang, Z. J. Zhang, Z. Y. Zhang, Z. P. Zhang, J. Zhao, L. Zhou, H. L. Zhuang, J. H. Zou

The Daya Bay experiment has observed correlations between reactor core fuel evolution and changes in the reactor antineutrino flux and energy spectrum. Four antineutrino detectors in two experimental halls were used to identify 2.2 million inverse beta decays (IBDs) over 1230 days spanning multiple fuel cycles for each of six 2. Read More

This work addresses the recovery and demixing problem of signals that are sparse in some general dictionary. Involved applications include source separation, image inpainting, super-resolution, and restoration of signals corrupted by clipping, saturation, impulsive noise, or narrowband interference. We employ the $\ell_q$-norm ($0 \le q < 1$) for sparsity inducing and propose a constrained $\ell_q$-minimization formulation for the recovery and demixing problem. Read More

We study the influence of external pressure on the electronic and magnetic structure of EuMnO3 from first-principles calculations. We find a pressure-induced insulator-metal transition at which the magnetic order changes from A-type antiferromagnetic to ferromagnetic with a strong interplay with Jahn-Teller distortions. In addition, we find that the non-centrosymmetric E*-type antiferromagnetic order can become nearly degenerate with the ferromagnetic ground state in the high-pressure metallic state. Read More

Based on the random matrix model, we can build statistical models using massive datasets across the power grid, and employ hypothesis testing for anomaly detection. First, the aim of this paper is to make the first attempt to apply the recent free probability result in extracting big data analytics, in particular data fusion. The nature of this work is basic in that new algorithms and analytics tools are proposed to pave the way for the future's research. Read More

The formation and migration energies for various point defects, including vacancies and self-interstitials in aluminum are reinvestigated systematically using the supercell approximation in the framework of orbital-free density functional theory. In particular, the finite-size effects and the accuracy of various kinetic energy density functionals are examined.The calculated results suggest that the errors due to the finite-size effect decrease exponentially upon enlarging the supercell. Read More

Data-driven methodologies are more suitable for a complex grid with readily accessible data when tasked with situation awareness. However, it is a challenge to turn the massive data, especially those with some spatial or temporal errors, into the driving force within tolerable cost of resources such as time and computation. This paper, based on random matrix theory (RMT), outlines a novel data-driven methodology. Read More

Analogous deployment of phase measurement units (PMUs), deregulation of energy market and the urge for power system state estimation, all call for stability assessment in modern power system. However, implementing a model based indicator is impracticable for the large scale power system for many reasons, such as difficulties in modelling the massive streaming PMU data and high computational complexity of solving the high dimension power flow equations. In this paper, we firstly represent massive streaming PMU data as big random matrix flow. Read More

The structural, electronic, mechanical, optical, thermodynamic properties of plutonium monoxide monohydride (PuOH) are studied by density-functional calculations within the framework of LDA/GGA and LDA/GGA+U.From the total energy calculation, the lowest-energy crystal structure of PuOH is predicted to have space group F-43m (No. 216). Read More

Uncertainties of fission fraction is an important uncertainty source for the antineutrino flux prediction in a reactor antineutrino experiment. A new MC-based method of evaluating the covariance coefficients between isotopes was proposed. It was found that the covariance coefficients will varying with reactor burnup and which may change from positive to negative because of fissioning balance effect, for example, the covariance coefficient between $^{235}$U and $^{239}$Pu changes from 0. Read More

A multiple input multiple output ultra-wideband cognitive radar based on compressive sensing is presented in this letter. For traditional UWB radar, high sampling rate analog to digital converter at the receiver is required to meet Shannon theorem, which increases hardware complexity. In order to bypass the bottleneck of ADC or further increase the radar bandwidth using the latest wideband ADC, we propose to exploit CS for signal reconstruction at the receiver of UWB radar for the sparse targets in the surveillance area. Read More

This work addresses the issue of large covariance matrix estimation in high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed. However, these algorithms cannot be directly extended to use a nonconvex penalty for sparsity inducing. Read More

The ability of background discrimination using pulse shape discrimination (PSD) in broad-energy germanium (BEGe) detectors makes them as competitive candidates for neutrinoless double beta decay (0{\nu}\b{eta}\b{eta}) experiments. The measurements of key parameters for detector modeling in a commercial p-type BEGe detector are presented in this paper. Point-like sources were used to investigate the energy resolution and linearity of the detector. Read More

The Auto-Importance Sampling (AIS) method is a Monte Carlo variance reduction technique proposed for deep penetration problems, which can significantly improve computational efficiency without pre-calculations for importance distribution. However, the AIS method is only validated with several simple examples, and cannot be used for coupled neutron-photon transport. This paper presents the improved algorithms for the AIS method, including particle transport, fictitious particles creation and adjustment, fictitious surface geometry, random number allocation and calculation of the estimated relative error. Read More

Future power grids are fundamentally different from current ones, both in size and in complexity; this trend imposes challenges for situation awareness (SA) based on classical indicators, which are usually model-based and deterministic. As an alternative, this paper proposes a statistical indicator system based on linear eigenvalue statistics (LESs) of large random matrices: 1) from a data modeling viewpoint, we build, starting from power flows equations, the random matrix models (RMMs) only using the real-time data flow in a statistical manner; 2) for a data analysis that is fully driven from RMMs, we put forward the high-dimensional indicators, called LESs that have some unique statistical features such as Gaussian properties; and 3) we develop a three-dimensional (3D) power-map to visualize the system, respectively, from a high-dimensional viewpoint and a low-dimensional one. Therefore, a statistical methodology of SA is employed; it conducts SA with a model-free and data-driven procedure, requiring no knowledge of system topologies, units operation/control models, causal relationship, etc. Read More

A new set of fluence-to-dose conversion coefficients based on the Chinese reference adult voxel phantoms CRAM and CRAF are presented for six idealized external neutron exposures from 10-8 MeV to 20 MeV. The voxel phantoms CRAM and CRAF were adjusted from the previous phantoms CNMAN and CNWM respectively, and the masses of individual organs have been adjusted to the Chinese reference data. The calculation of organ-absorbed doses and effective doses were performed with the Monte Carlo transport code MCNPX. Read More

To accommodate current machine type communications (MTC), an uplink waveform is proposed where MTC nodes use one carrier to transmit signal, and central nodes demodulate different nodes' signal jointly. Furthermore, the carrier bandwidth is variable to fit for the channels of nodes. This waveform may reduce the hardware complexity of low cost MTC nodes, and loose the time and frequency domain synchronization requirements of the entire system. Read More

How the two dimensional (2D) quantum Wigner crystal (WC) transforms into the metallic liquid phase remains to be an outstanding problem in physics. In theories considering the 2D WC to liquid transition in the clean limit, it was suggested that a number of intermediate phases might exist. We have studied the transformation between the metallic fluid phase and the low magnetic field reentrant insulating phase (RIP) which was interpreted as due to WC formation [Qiu et al, PRL 108, 106404 (2012)], in a strongly correlated 2D hole system with large interaction parameter $r_s$ ($\sim~$20-30) and high mobility. Read More

From the view of Heegaard splitting, it is known that if a closed orientable 3-manifold admits a distance at least three Heegaard splitting, then it is hyperbolic. However, for a closed orientable 3-manifold admitting only distance at most two Heegaard splittings, there are examples shows that it could be reducible, Seifert, toroidal or hyperbolic. According to Thurston's Geometrization conjecture, the most important piece of eight geometries is hyperbolic. Read More

The operating status of power systems is influenced by growing varieties of factors, resulting from the developing sizes and complexity of power systems; in this situation, the modelbased methods need be revisited. A data-driven method, as the novel alternative, on the other hand, is proposed in this paper: it reveals the correlations between the factors and the system status through statistical properties of data. An augmented matrix, as the data source, is the key trick for this method; it is formulated by two parts: 1) status data as the basic part, and 2) factor data as the augmented part. Read More

The paper has two parts. The first one deals with how to use large random matrices as building blocks to model the massive data arising from the massive (or large-scale) MIMO system. As a result, we apply this model for distributed spectrum sensing and network monitoring. Read More

Data with features of volume, velocity, variety, and veracity are challenging traditional tools to extract useful analysis for decision-making. By integrating high-dimensional analysis with visualization, this paper develops a 3D power-map animation as an effective solution to the challenge. An architecture design, with detailed data processing procedure, is proposed to realize the integration. Read More

Power systems are developing very fast nowadays, both in size and in complexity; this situation is a challenge for Early Event Detection (EED). This paper proposes a data- driven unsupervised learning method to handle this challenge. Specifically, the random matrix theories (RMTs) are introduced as the statistical foundations for random matrix models (RMMs); based on the RMMs, linear eigenvalue statistics (LESs) are defined via the test functions as the system indicators. Read More

This paper presents the design and implementation of a novel SDR based massive MIMO testbed with up to 70 nodes built at Tennessee Technological University. The deployment can reach a $30 \times 30$ antenna MIMO scheme. With this testbed, we are able to measure the channel matrix and compute the achievable rate of the massive MIMO system using experimental data. Read More

The central theme of this talk is to promote the non-asymptotic statistical viewpoint in the context of massive datasets. The classical viewpoint breaks down when the data size becomes large. Read More

This notes explores angle structures on ideally triangulated compact $3$-manifolds with high genus boundary. We show that the existence of angle structures implies the existence of a hyperbolic metric with totally geodesic boundary, and conversely each hyperbolic $3$-manifold with totally geodesic boundary has an ideal triangulation that admits angle structures. Read More

Spectrum sensing is a fundamental component of cognitive radio. How to promptly sense the presence of primary users is a key issue to a cognitive radio network. The time requirement is critical in that violating it will cause harmful interference to the primary user, leading to a system-wide failure. Read More

The aim of this paper is to study data modeling for massive datasets. Large random matrices are used to model the massive amount of data collected from our experimental testbed. This testbed was developed for a real-time ultra-wideband, multiple input multiple output (UWB-MIMO) system. Read More

This short paper reports some initial experimental demonstrations of the theoretical framework: the massive amount of data in the large-scale cognitive radio network can be naturally modeled as (large) random matrices. In particular, using experimental data we will demonstrate that the empirical spectral distribution of the large sample covariance matrix---a Hermitian random matrix---agree with its theoretical distribution (Marchenko-Pastur law). On the other hand, the eigenvalues of the large data matrix ---a non-Hermitian random matrix---are experimentally found to follow the single ring law, a theoretical result that has been discovered relatively recently. Read More

We show that, for any integer $n\ge 3$, there is a prime knot $k$ such that (1) $k$ is not meridionally primitive, and (2) for every $m$-bridge knot $k'$ with $m\leq n$, the tunnel numbers satisfy $t(k\# k')\le t(k)$. This gives counterexamples to a conjecture of Morimoto and Moriah on tunnel number under connected sum and meridionally primitive knots. Read More

A topological phase can often be represented by a corresponding wavefunction (exact eigenstate of a model Hamiltonian) that has a higher underlying symmetry than necessary. When the symmetry is explicitly broken in the Hamiltonian, the model wavefunction fails to account for the change due to the lack of a variational parameter. Here we exemplify the case by an integer quantum Hall system with anisotropic interaction. Read More

We report electrical conductance and thermopower measurements on InAs nanowires synthesized by chemical vapor deposition. Gate modulation of the thermopower of individual InAs nanowires with diameter around 20nm is obtained over T=40 to 300K. At low temperatures (T< ~100K), oscillations in the thermopower and power factor concomitant with the stepwise conductance increases are observed as the gate voltage shifts the chemical potential of electrons in InAs nanowire through quasi-one-dimensional (1D) sub-bands. Read More

We report the composition and gate voltage induced tuning of transport properties in chemically synthesized Bi2(Te1-xSex)3 nanoribbons. It is found that increasing Se concentration effectively suppresses the bulk carrier transport and induces semiconducting behavior in the temperature dependent resistance of Bi2(Te1-xSex)3 nanoribbons when x is greater than ~10%. In Bi2(Te1-xSex)3 nanoribbons with x ~20%, gate voltage enables ambipolar modulation of resistance (or conductance) in samples with thickness around or larger than 100nm, indicating significantly enhanced contribution in transport from the gapless surface states. Read More

Topological insulators are novel quantum materials with metallic surface transport, but insulating bulk behavior. Often, topological insulators are dominated by bulk contributions due to defect induced bulk carriers, making it difficult to isolate the more interesting surface transport characteristics. Here, we report the synthesis and characterization of nanosheets of topological insulator Bi2Se3 with variable Sb-doping level to control the electron carrier density and surface transport behavior. Read More

In this paper, we prove that (1) For any integers $n\geq 1$ and $g\geq 2$, there is a closed 3-manifold $M_{g}^{n}$ which admits a distance $n$ Heegaard splitting of genus $g$ except that the pair of $(g, n)$ is $(2, 1)$. Furthermore, $M_{g}^{n}$ can be chosen to be hyperbolic except that the pair of $(g, n)$ is $(3, 1)$. (2) For any integers $g\geq 2$ and $n\geq 4$, there are infinitely many non-homeomorphic closed 3-manifolds admitting distance $n$ Heegaard splittings of genus $g$. Read More

In this paper, we consider the problem of mean first-passage time (MFPT) in quantum mechanics; the MFPT is the average time of the transition from a given initial state, passing through some intermediate states, to a given final state for the first time. We apply the method developed in statistical mechanics for calculating the MFPT of random walks to calculate the MFPT of a transition process. As applications, we (1) calculate the MFPT for multiple-state systems, (2) discuss transition processes occurring in an environment background, (3) consider a roundabout transition in a hydrogen atom, and (4) apply the approach to laser theory. Read More

Spectrum sensing is a fundamental problem in cognitive radio. We propose a function of covariance matrix based detection algorithm for spectrum sensing in cognitive radio network. Monotonically increasing property of function of matrix involving trace operation is utilized as the cornerstone for this algorithm. Read More

Model quantum Hall states including Laughlin, Moore-Read and Read-Rezayi states are generalized into appropriate anisotropic form. The generalized states are exact zero-energy eigenstates of corresponding anisotropic two- or multi-body Hamiltonians, and explicitly illustrate the existence of geometric degrees of in the fractional quantum Hall effect. These generalized model quantum Hall states can provide a good description of the quantum Hall system with anisotropic interactions. Read More

We present the transport and capacitance measurements of 10nm wide GaAs quantum wells with hole densities around the critical point of the 2D metal-insulator transition (critical density $p_c$ down to 0.8$\times10^{10}$/cm$^2$, $r_s\sim$36). For metallic hole density $p_c < p Read More

There is a trend of applying machine learning algorithms to cognitive radio. One fundamental open problem is to determine how and where these algorithms are useful in a cognitive radio network. In radar and sensing signal processing, the control of degrees of freedom (DOF)---or dimensionality---is the first step, called pre-processing. Read More

Kernel method is a very powerful tool in machine learning. The trick of kernel has been effectively and extensively applied in many areas of machine learning, such as support vector machine (SVM) and kernel principal component analysis (kernel PCA). Kernel trick is to define a kernel function which relies on the inner-product of data in the feature space without knowing these feature space data. Read More

It has been puzzling that the resistivity of high mobility two-dimensional(2D) carrier systems in semiconductors with low carrier density often exhibits a large increase followed by a decrease when the temperature ($T$) is raised above a characteristic temperature comparable with the Fermi temperature ($T_F$). We find that the metallic 2D hole system (2DHS) in GaAs quantum well (QW) has a linear density ($p$) dependent conductivity, $\sigma\approx e\mu^*(p-p_0)$, in both the degenerate (T<Read More

We investigate fast rotating quasi-two-dimensional dipolar Fermi gases in the quantum Hall regime. By tuning the direction of the dipole moments with respect to the z-axis, the dipole-dipole interaction becomes anisotropic in the $x$-$y$ plane. For a soft confining potential we find that, as we tilt the angle of the dipole moments, the system evolves from a $\nu = 1/3$ Laughlin state with dipoles being polarized along the z axis to a series of ground states characterized by distinct mean total angular momentum, and finally to an anisotropic integer quantum Hall state. Read More

Prior knowledge can improve the performance of spectrum sensing. Instead of using universal features as prior knowledge, we propose to blindly learn the localized feature at the secondary user. Motivated by pattern recognition in machine learning, we define signal feature as the leading eigenvector of the signal's sample covariance matrix. Read More

We investigate the dynamical properties of a trapped finite-temperature normal Fermi gas with dipole-dipole interaction. For the free expansion dynamics, we show that the expanded gas always becomes stretched along the direction of the dipole moment. In addition, we present the temperature and interaction dependences of the asymptotical aspect ratio. Read More

Spectrum sensing is essential in cognitive radio. By defining leading \textit{eigenvector} as feature, we introduce a blind feature learning algorithm (FLA) and a feature template matching (FTM) algorithm using learned feature for spectrum sensing. We implement both algorithms on Lyrtech software defined radio platform. Read More

Various primary user (PU) radios have been allocated into fixed frequency bands in the whole spectrum. A cognitive radio network (CRN) should be able to perform the wideband spectrum sensing (WSS) to detect temporarily unoccupied frequency bands. We summarize four occupancy features for the frequency bands. Read More

Spectrum sensing is the major challenge in the cognitive radio (CR). We propose to learn local feature and use it as the prior knowledge to improve the detection performance. We define the local feature as the leading eigenvector derived from the received signal samples. Read More

Sampling rate is the bottleneck for spectrum sensing over multi-GHz bandwidth. Recent progress in compressed sensing (CS) initialized several sub-Nyquist rate approaches to overcome the problem. However, efforts to design CS reconstruction algorithms for wideband spectrum sensing are very limited. Read More

We report the study of a novel linear magneto-resistance (MR) under perpendicular magnetic fields in Bi2Se3 nanoribbons. Through angular dependence magneto-transport experiments, we show that this linear MR is purely due to two-dimensional (2D) transport, in agreement with the recently discovered linear MR from 2D topological surface state in bulk Bi2Te3, and the linear MR of other gapless semiconductors and graphene. We further show that the linear MR of Bi2Se3 nanoribbons persists to room temperature, underscoring the potential of exploiting topological insulator nanomaterials for room temperature magneto-electronic applications. Read More

The communication for the control of distributed energy generation (DEG) in microgrid is discussed. Due to the requirement of realtime transmission, weak or no explicit channel coding is used for the message of system state. To protect the reliability of the uncoded or weakly encoded messages, the system dynamics are considered as a `nature encoding' similar to convolution code, due to its redundancy in time. Read More