# Qing Zhou

## Contact Details

NameQing Zhou |
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## Pubs By Year |
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## Pub CategoriesStatistics - Methodology (9) Mathematics - Geometric Topology (8) Statistics - Machine Learning (7) Mathematics - Information Theory (6) Computer Science - Information Theory (6) Statistics - Theory (5) Mathematics - Statistics (5) Statistics - Applications (3) Mathematics - Probability (3) Computer Science - Learning (3) Statistics - Computation (2) Computer Science - Networking and Internet Architecture (2) Mathematics - Dynamical Systems (2) Physics - Computational Physics (1) Quantum Physics (1) Mathematics - Number Theory (1) Astrophysics (1) Physics - Disordered Systems and Neural Networks (1) Physics - Mesoscopic Systems and Quantum Hall Effect (1) Physics - Materials Science (1) Computer Science - Logic in Computer Science (1) Mathematics - Combinatorics (1) Physics - Statistical Mechanics (1) Computer Science - Discrete Mathematics (1) Quantitative Biology - Genomics (1) |

## Publications Authored By Qing Zhou

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets typically have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we develop a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. Read More

The output of an automated theorem prover is usually presented by using a text format, they are often too heavy to be understood. In model checking setting, it would be helpful if one can observe the structure of models and the verification procedures. A 3D visualization tool (\textsf{VMDV}) is proposed in this paper to address these problems. Read More

To make inference about a group of parameters on high-dimensional data, we develop the method of estimator augmentation for the block Lasso, which is defined via the block norm. By augmenting a block Lasso estimator $\hat{\beta}$ with the subgradient $S$ of the block norm evaluated at $\hat{\beta}$, we derive a closed-form density for the joint distribution of $(\hat{\beta},S)$ under a high-dimensional setting. This allows us to draw from an estimated sampling distribution of $\hat{\beta}$, or more generally any function of $(\hat{\beta},S)$, by Monte Carlo algorithms. Read More

We consider the problem of estimating a directed acyclic graph (DAG) for a multivariate normal distribution from high-dimensional data with $p\gg n$. Our main results establish nonasymptotic deviation bounds on the estimation error, sparsity bounds, and model selection consistency for a penalized least squares estimator under concave regularization. The proofs rely on interpreting the graphical model as a recursive linear structural equation model, which reduces the estimation problem to a series of tractable neighbourhood regressions and allows us to avoid making any assumptions regarding faithfulness. Read More

We consider the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in a wireless sensor network (WSN), where each sensor receives a single snapshot of the field. We assume that the observation at each node randomly falls into one of two modes: a valid or an invalid observation mode. Specifically, mode one corresponds to the desired signal plus noise observation mode (\emph{valid}), and mode two corresponds to the pure noise mode (\emph{invalid}) due to node defect or damage. Read More

This paper investigates the problem of energy-efficient packet transmission with arbitrary arrival instants and deadline constraints over a point-to-point Additive White Gaussian Noise (AWGN) channel. This is different from previous work where it is assumed that the packets follow a First-In-First-Out (FIFO) order in that the packets that arrive earlier will have a deadline that is also earlier. We first investigate the necessary and sufficient conditions of the optimal transmission scheduler. Read More

This paper investigates the problem of energy-efficient packet transmission with a non-FIFO Packet over a point-to-point additive white Gaussian noise (AWGN) time-invariant channel under the feasibility constraints. More specifically, we consider the scenario where there is a packet that has a deadline that is earlier than that of the previously arrived packet. For this problem, the First-In-First-Out (FIFO) transmission mode adopted in the existing literatures is no longer optimal. Read More

Quantifying the uncertainty in penalized regression under group sparsity, such as the group Lasso, is an important, yet still open, question. We establish, under a high-dimensional scaling, the asymptotic validity of a modified parametric bootstrap method for the group Lasso, assuming a Gaussian error model and mild conditions on the design matrix and the true coefficients. Consequently, simulation of bootstrap samples provides a convenient means of making inference on large groups of coefficients simultaneously. Read More

Graphane is graphene fully hydrogenated from both sides, forming a 1x1 structure, where all C atoms are in sp3 configuration. In silicene, the Si atoms are in a mix-sp2/sp3 configuration, it is therefore natural to imagine silicane in analogue to graphane. However, monoatomic silicene sheet grown on substrates generally reconstructs into different phases, and only partially hydrogenated silicene with reconstructions had been reported before. Read More

We develop an iterative subsampling approach to improve the computational efficiency of our previous work on solution path clustering (SPC). The SPC method achieves clustering by concave regularization on the pairwise distances between cluster centers. This clustering method has the important capability to recognize noise and to provide a short path of clustering solutions; however, it is not sufficiently fast for big datasets. Read More

Fast accumulation of large amounts of complex data has created a need for more sophisticated statistical methodologies to discover interesting patterns and better extract information from these data. The large scale of the data often results in challenging high-dimensional estimation problems where only a minority of the data shows specific grouping patterns. To address these emerging challenges, we develop a new clustering methodology that introduces the idea of a regularization path into unsupervised learning. Read More

This paper considers the problem of partially observed optimal control for forward stochastic systems which are driven by Brownian motions and an independent Poisson random measure with a feature that the cost functional is of mean-field type. When all the system coefficients and the objective performance functionals are allowed to be random, possibly non-Markovian, Malliavin calculus is employed to derive a maximum principle for the optimal control of such a system where the adjointprocess is explicitly expressed. We also investigate the mean-field type optimal control problems for systems driven by mean-field type stochastic differential equations (SDEs in short) with jump processes, in which the coefficients contain not only the state process but also its marginal distribution under partially observed information. Read More

We develop in this article a penalized likelihood method to estimate sparse Bayesian networks from categorical data. The structure of a Bayesian network is represented by a directed acyclic graph (DAG). We model the conditional distribution of a node given its parents by multi-logit regression and estimate the structure of a DAG via maximizing a regularized likelihood. Read More

Regularized linear regression under the $\ell_1$ penalty, such as the Lasso, has been shown to be effective in variable selection and sparse modeling. The sampling distribution of an $\ell_1$-penalized estimator $\hat{\beta}$ is hard to determine as the estimator is defined by an optimization problem that in general can only be solved numerically and many of its components may be exactly zero. Let $S$ be the subgradient of the $\ell_1$ norm of the coefficient vector $\beta$ evaluated at $\hat{\beta}$. Read More

We develop a penalized likelihood estimation framework to estimate the structure of Gaussian Bayesian networks from observational data. In contrast to recent methods which accelerate the learning problem by restricting the search space, our main contribution is a fast algorithm for score-based structure learning which does not restrict the search space in any way and works on high-dimensional datasets with thousands of variables. Our use of concave regularization, as opposed to the more popular $\ell_0$ (e. Read More

This paper first studies the homogeneous 3-user 2x1 broadcast channel (BC) with no CSIT. We show a sufficient condition for it to achieve the optimal 3/2 degrees of freedom (DoF) by using Blind Interference Alignment (BIA). BIA refers to the interference alignment method without the need of CSIT. Read More

Although the sufficient condition for a blindly interference-aligned (BIA) 2-user 2x1 broadcast channel (BC) in homogeneous fading to achieve its maximal 4/3 DoF is well understood, its counterpart for the general K-user 2x1 MISO BC in homogeneous block fading to achieve the corresponding 2k/(2+K-1) (DoF) remains unsolved and is, thus, the focus of this paper. An interference channel is said BIA-feasible if it achieves its maximal DoF only via BIA. In this paper, we cast this general feasibility problem in the framework of finding integer solutions for a system of linear Diophantine equations. Read More

Motivated by applications such as battery-operated wireless sensor networks (WSN), we propose an easy-to-implement energy-efficient two-way relaying scheme. In particular, we address the challenge of improving the standard two-way selective decode-and-forward protocol (TW-SDF) in terms of block-error-rate (BLER) with minor additional complexity and energy consumption. By following the principle of soft relaying, our solution is the two-way one-bit soft forwarding (TW-1bSF) protocol in which the relay forwards the one-bit quantization of a posterior information metric about the transmitted bits, associated with an appropriately designed reliability parameter. Read More

Staggered fading pattern between different users is crucial to interference alignment without CSIT, or so-called blind interference alignment (BIA). This special fading structure naturally arises from heterogeneous block fading setting, in which different users experience independent block fading with different coherent times. Jafar et al. Read More

A {\it path covering} of a graph $G$ is a set of vertex disjoint paths of $G$ containing all the vertices of $G$. The {\it path covering number} of $G$, denoted by $P(G)$, is the minimum number of paths in a path covering of $G$. An {\sl $k$-L(2,1)-labeling} of a graph $G$ is a mapping $f$ from $V(G)$ to the set ${0,1,. Read More

When a posterior distribution has multiple modes, unconditional expectations, such as the posterior mean, may not offer informative summaries of the distribution. Motivated by this problem, we propose to decompose the sample space of a multimodal distribution into domains of attraction of local modes. Domain-based representations are defined to summarize the probability masses of and conditional expectations on domains of attraction, which are much more informative than the mean and other unconditional expectations. Read More

An efficient algorithm is developed to construct disconnectivity graphs by a random walk over basins of attraction. This algorithm can detect a large number of local minima, find energy barriers between them, and estimate local thermal averages over each basin of attraction. It is applied to the SK spin glass Hamiltonian where existing methods have difficulties even for a moderate number of spins. Read More

In this paper, a class of generalized backward doubly stochastic differential equations whose coefficient contains the subdifferential operators of two convex functions (also called generalized backward doubly stochastic variational inequalities) are considered. By means of a penalization argument based on Yosida approximation, we establish the existence and uniqueness of the solution. As an application, this result is used to derive existence result of stochastic viscosity solution for a class of multivalued stochastic Dirichlet-Neumann problems. Read More

The problem of motif detection can be formulated as the construction of a discriminant function to separate sequences of a specific pattern from background. In computational biology, motif detection is used to predict DNA binding sites of a transcription factor (TF), mostly based on the weight matrix (WM) model or the Gibbs free energy (FE) model. However, despite the wide applications, theoretical analysis of these two models and their predictions is still lacking. Read More

Defining the energy function as the negative logarithm of the density, we explore the energy landscape of a distribution via the tree of sublevel sets of its energy. This tree represents the hierarchy among the connected components of the sublevel sets. We propose ways to annotate the tree so that it provides information on both topological and statistical aspects of the distribution, such as the local energy minima (local modes), their local domains and volumes, and the barriers between them. Read More

Cis-regulatory modules (CRMs) composed of multiple transcription factor binding sites (TFBSs) control gene expression in eukaryotic genomes. Comparative genomic studies have shown that these regulatory elements are more conserved across species due to evolutionary constraints. We propose a statistical method to combine module structure and cross-species orthology in de novo motif discovery. Read More

Solenoids are ``inverse limits'' of the circle, and the classical knot theory is the theory of tame embeddings of the circle into the 3-space. We give some general study, including certain classification results, of tame embeddings of solenoids into the 3-space as the ``inverse limits'' of the tame embeddings of the circle. Some applications are discussed. Read More

Rejoinder to ``Equi-energy sampler with applications in statistical inference and statistical mechanics'' by Kou, Zhou and Wong [math.ST/0507080] Read More

We introduce a new sampling algorithm, the equi-energy sampler, for efficient statistical sampling and estimation. Complementary to the widely used temperature-domain methods, the equi-energy sampler, utilizing the temperature--energy duality, targets the energy directly. The focus on the energy function not only facilitates efficient sampling, but also provides a powerful means for statistical estimation, for example, the calculation of the density of states and microcanonical averages in statistical mechanics. Read More

We say a knot $k$ in the 3-sphere $\mathbb S^3$ has {\it Property $IE$} if the infinite cyclic cover of the knot exterior embeds into $\mathbb S^3$. Clearly all fibred knots have Property $IE$. There are infinitely many non-fibred knots with Property $IE$ and infinitely many non-fibred knots without property $IE$. Read More

This paper has been withdrawn by author due to an error in the proof. Read More

For any hyperbolic 3-manifold $M$ with totally geodesic boundary, there are finitely many boundary slopes for essential immersed surfaces of a given genus. There is a uniform bound for the number of such boundary slopes if the genus of $\partial M$ or the volume of $M$ is bounded above. When the volume is bounded above, then area of $\partial M$ is bounded above and the length of closed geodesic on $\partial M$ is bounded below. Read More

We address a conjecture that $\pi_1$-surjective maps between closed aspherical 3-manifolds having the same rank on $\pi_1$ must be of non-zero degree. The conjecture is proved for Seifert manifolds, which is used in constructing the first known example of minimum Haken manifold. Another motivation is to study epimorphisms of 3-manifold groups via maps of non-zero degree between 3-manifolds. Read More

In this paper, we proved that any closed orientable 3-manifold 1-dominates at most finitely many geometric 3-manifolds. Read More

The effect of the laser linewidth on the resonance fluorescence spectrum of a two-level atom is revisited. The novel spectral features, such as hole-burning and dispersive profiles at line centre of the fluorescence spectrum are predicted when the laser linewidth is much greater than its intensity. The unique features result from quantum interference between different dressed-state transition channels. Read More

Let $\Sigma$ be a hyperbolic link with $m$ components in a 3-dimensional manifold $X$. In this paper, we will show that the moduli space of marked hyperbolic cone structures on the pair $(X, \Sigma)$ with all cone angle less than $2\pi /3$ is an $m$-dimensional open cube, parameterized naturally by the $m$ cone angles. As a corollary, we will give a proof of a special case of Thurston's geometrization theorem for orbifolds. Read More

Given a pair of curves C_1 and C_2 on a hyperbolic surface F, when does there exist a pseudo-Anosov map sending one to another? More generally, one may ask the same question for C_i to be sets of disjoint simple closed curves. We will give necessary and sufficient conditions for the existence of such maps. Read More