Results for "Rong Ge"

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Individualized Group LearningJun 13 2019Many massive data are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significant ... More
Faster Algorithms for High-Dimensional Robust Covariance EstimationJun 11 2019We study the problem of estimating the covariance matrix of a high-dimensional distribution when a small constant fraction of the samples can be arbitrarily corrupted. Recent work gave the first polynomial time algorithms for this problem with near-optimal ... More
Reply to "Comment on "Normalization of quasinormal modes in leaky optical cavities and plasmonic resonators" " by E. A. Muljarov and W. LangbeinMay 16 2016We refute all claims of the "Comment on "Normalization of quasinormal modes in leaky optical cavities and plasmonic resonators" " by E. A. Muljarov and W. Langbein (arXiv:1602.07278v1). Based entirely on information already contained in our original article ... More
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate ProcedureApr 29 2019There is a stark disparity between the step size schedules used in practical large scale machine learning and those that are considered optimal by the theory of stochastic approximation. In theory, most results utilize polynomially decaying learning rate ... More
Reinforcement Learning for Nested Polar Code ConstructionApr 16 2019In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques. First, an MDP environment with state, action, and reward is defined in the context of polar ... More
Rich Component AnalysisJul 14 2015In many settings, we have multiple data sets (also called views) that capture different and overlapping aspects of the same phenomenon. We are often interested in finding patterns that are unique to one or to a subset of the views. For example, we might ... More
Deep Reinforcement Learning for Scheduling in Cellular NetworksMay 15 2019Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia. A common solution is to replace partial or even all modules in the conventional systems, which is often lack of efficiency and ... More
On the Optimization Landscape of Tensor DecompositionsJun 18 2017Non-convex optimization with local search heuristics has been widely used in machine learning, achieving many state-of-art results. It becomes increasingly important to understand why they can work for these NP-hard problems on typical data. The landscape ... More
Efficient approaches for escaping higher order saddle points in non-convex optimizationFeb 18 2016Local search heuristics for non-convex optimizations are popular in applied machine learning. However, in general it is hard to guarantee that such algorithms even converge to a local minimum, due to the existence of complicated saddle point structures ... More
Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares AlgorithmsApr 21 2015Tensor rank and low-rank tensor decompositions have many applications in learning and complexity theory. Most known algorithms use unfoldings of tensors and can only handle rank up to $n^{\lfloor p/2 \rfloor}$ for a $p$-th order tensor in $\mathbb{R}^{n^p}$. ... More
Understanding Composition of Word Embeddings via Tensor DecompositionFeb 02 2019Word embedding is a powerful tool in natural language processing. In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase. We give a generative model that ... More
A Practical Algorithm for Topic Modeling with Provable GuaranteesDec 19 2012Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood objective. Efficient algorithms exist that approximate ... More
Intersecting Faces: Non-negative Matrix Factorization With New GuaranteesJul 08 2015Non-negative matrix factorization (NMF) is a natural model of admixture and is widely used in science and engineering. A plethora of algorithms have been developed to tackle NMF, but due to the non-convex nature of the problem, there is little guarantee ... More
Non-Convex Matrix Completion Against a Semi-Random AdversaryMar 28 2018Sep 07 2018Matrix completion is a well-studied problem with many machine learning applications. In practice, the problem is often solved by non-convex optimization algorithms. However, the current theoretical analysis for non-convex algorithms relies heavily on ... More
Incorporating Consistency Verification into Neural Data-to-Document GenerationAug 15 2018Aug 18 2018Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts. However, large proportions of generated texts do not actually conform to the input data. To address this issue, we propose ... More
Operations Guided Neural Networks for High Fidelity Data-To-Text GenerationSep 08 2018Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent ... More
Quasinormal mode theory and modelling of electron energy loss spectroscopy for plasmonic nanostructuresOct 05 2015Mar 20 2016Understanding light-matter interactions using localized surface plasmons (LSPs) is of fundamental interest in classical and quantum plasmonics and has a wide range of applications. In order to understand the spatial properties of LSPs, electron energy ... More
Design of an efficient single photon source from a metallic nanorod dimer: a quasinormal mode finite-difference time-domain approachJan 23 2015We describe how the finite-difference time-domain (FDTD) technique can be used to compute the quasinormal mode (QNM) for metallic nano-resonators, which is important for describing and understanding light-matter interactions in nanoplasmonics. We use ... More
Quantum dynamics of two quantum dots coupled through localized plasmons: An intuitive and accurate quantum optics approach using quasinormal modesMay 08 2015Nov 17 2015We study the quantum dynamics of two quantum dots (QDs) or artificial atoms coupled through the fundamental localized plasmon of a gold nanorod resonator. We derive an intuitive and efficient time-local master equation, in which the effect of the metal ... More
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte CarloOct 07 2017Nov 06 2017A key task in Bayesian statistics is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). However, without any assumptions, sampling (even approximately) can be #P-hard, and few works have ... More
Learning One-hidden-layer Neural Networks with Landscape DesignNov 01 2017Nov 03 2017We consider the problem of learning a one-hidden-layer neural network: we assume the input $x\in \mathbb{R}^d$ is from Gaussian distribution and the label $y = a^\top \sigma(Bx) + \xi$, where $a$ is a nonnegative vector in $\mathbb{R}^m$ with $m\le d$, ... More
Analyzing Tensor Power Method Dynamics in Overcomplete RegimeNov 06 2014Sep 14 2015We present a novel analysis of the dynamics of tensor power iterations in the overcomplete regime where the tensor CP rank is larger than the input dimension. Finding the CP decomposition of an overcomplete tensor is NP-hard in general. We consider the ... More
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric AnalysisApr 03 2017In this paper we develop a new framework that captures the common landscape underlying the common non-convex low-rank matrix problems including matrix sensing, matrix completion and robust PCA. In particular, we show for all above problems (including ... More
Matrix Completion has No Spurious Local MinimumMay 24 2016Sep 16 2016Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algorithms are popular and effective in practice. Despite recent progress in ... More
Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain DecompositionNov 29 2018A key task in Bayesian machine learning is sampling from distributions that are only specified up to a partition function (i.e., constant of proportionality). One prevalent example of this is sampling posteriors in parametric distributions, such as latent-variable ... More
Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor MethodsAug 03 2014Dec 16 2014We provide guarantees for learning latent variable models emphasizing on the overcomplete regime, where the dimensionality of the latent space can exceed the observed dimensionality. In particular, we consider multiview mixtures, spherical Gaussian mixtures, ... More
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-$1$ UpdatesFeb 21 2014Mar 04 2015In this paper, we provide local and global convergence guarantees for recovering CP (Candecomp/Parafac) tensor decomposition. The main step of the proposed algorithm is a simple alternating rank-$1$ update which is the alternating version of the tensor ... More
High-Dimensional Robust Mean Estimation in Nearly-Linear TimeNov 23 2018We study the fundamental problem of high-dimensional mean estimation in a robust model where a constant fraction of the samples are adversarially corrupted. Recent work gave the first polynomial time algorithms for this problem with dimension-independent ... More
Matrix Completion has No Spurious Local MinimumMay 24 2016Jul 22 2018Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algorithms are popular and effective in practice. Despite recent progress in ... More
New Algorithms for Learning Incoherent and Overcomplete DictionariesAug 28 2013May 26 2014In sparse recovery we are given a matrix $A$ (the dictionary) and a vector of the form $A X$ where $X$ is sparse, and the goal is to recover $X$. This is a central notion in signal processing, statistics and machine learning. But in applications such ... More
Learning Topic Models - Going beyond SVDApr 09 2012Apr 10 2012Topic Modeling is an approach used for automatic comprehension and classification of data in a variety of settings, and perhaps the canonical application is in uncovering thematic structure in a corpus of documents. A number of foundational works both ... More
Learning Mixtures of Gaussians in High DimensionsMar 02 2015Mar 10 2015Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn, the learning problem asks to estimate the means and the covariance matrices ... More
Towards a better approximation for sparsest cut?Apr 11 2013We give a new $(1+\epsilon)$-approximation for sparsest cut problem on graphs where small sets expand significantly more than the sparsest cut (sets of size $n/r$ expand by a factor $\sqrt{\log n\log r}$ bigger, for some small $r$; this condition holds ... More
SDSS-IV MaNGA: A Distinct Mass Distribution Explored in Slow-Rotating Early-type GalaxiesDec 18 2017Mar 01 2018We study the radial acceleration relation (RAR) for early-type galaxies (ETGs) in the SDSS MaNGA MPL5 dataset. The complete ETG sample show a slightly offset RAR from the relation reported by McGaugh et al. (2016) at the low-acceleration end; we find ... More
Conditional estimates on small distances between ordinates of zeros of $ζ(s)$ and $ζ'(s)$Apr 14 2016Let $\beta'+i\gamma'$ be a zero of $\zeta'(s)$. In \cite{GYi} Garaev and Y{\i}ld{\i}r{\i}m proved that there is a zero $\beta+i\gamma$ of $\zeta(s)$ with $ \gamma'-\gamma \ll \sqrt{|\beta'-1/2|} $. Assuming RH, we improve this bound by saving a factor ... More
Distinguished Capabilities of Artificial Intelligence Wireless Communication SystemsSep 15 2018With the great success of artificial intelligence (AI) technologies in pattern recognitions and signal processing, it is interesting to introduce AI technologies into wireless communication systems. Currently, most of studies are focused on applying AI ... More
DDVV-type inequality for skew-symmetric matrices and Simons-type inequality for Riemannian submersionsSep 30 2011Mar 12 2013In this paper, we will first derive a DDVV-type optimal inequality for real skew-symmetric matrices, then we apply it to establish a Simons-type integral inequality for Riemannian submersions with totally geodesic fibres and Yang-Mills horizontal distributions. ... More
Soul Theorem for 4-dimensional Topologically Regular Open Nonnegatively Curved Alexandrov SpacesOct 09 2010In this paper, we study the topology of topologically regular 4-dimensional open non-negatively curved Alexandrov spaces. These spaces occur naturally as the blow-up limits of compact Riemannian manifolds with lower curvature bound. These manifolds have ... More
First CT-MRI Scanner for Multi-dimensional Synchrony and Multi-physical CouplingFeb 11 2014We propose to prototype the first CT-MRI scanner for radiation therapy and basic research, demonstrate its transformative biomedical potential, and initiate a paradigm shift in multimodality imaging. Our design consists of a double donut-shaped pair of ... More
The Stretch Factor of the Delaunay Triangulation Is Less Than 1.998Mar 22 2011Jun 04 2013Let $S$ be a finite set of points in the Euclidean plane. Let $D$ be a Delaunay triangulation of $S$. The {\em stretch factor} (also known as {\em dilation} or {\em spanning ratio}) of $D$ is the maximum ratio, among all points $p$ and $q$ in $S$, of ... More
Anomalous minimum and scaling behavior of localization length near an isolated flat bandSep 02 2015Feb 12 2017Using one-dimensional tight-binding lattices and an analytical expression based on the Green's matrix, we show that anomalous minimum of the localization length near an isolated flat band, previously found for evanescent waves in a defect-free photonic ... More
Isoparametric foliations, diffeomorphism groups and exotic smooth structuresApr 24 2014Apr 20 2015In this paper, we are concerned with interactions between isoparametric theory and differential topology. Two foliations are called equivalent if there exists a diffeomorphism between the foliated manifolds mapping leaves to leaves. Using differential ... More
On submanifolds whose tubular hypersurfaces have constant mean curvaturesSep 30 2011Motivated by the theory of isoparametric hypersurfaces, we study submanifolds whose tubular hypersurfaces have some constant "higher order mean curvatures". Here a $k$-th order mean curvature $Q_k$ ($k\geq1$) of a hypersurface $M^n$ is defined as the ... More
Kazdan-Warner equation on graph in the negative caseNov 28 2016Let $G=(V,E)$ be a connected finite graph. In this short paper, we reinvestigate the Kazdan-Warner equation $$\Delta u=c-he^u$$ with $c<0$ on $G$, where $h$ defined on $V$ is a known function. Grigor'yan, Lin and Yang \cite{GLY} showed that if the Kazdan-Warner ... More
A Parameter-Free Learning Automaton SchemeNov 28 2017For a learning automaton, a proper configuration of its learning parameters, which are crucial for the automaton's performance, is relatively difficult due to the necessity of a manual parameter tuning before real applications. To ensure a stable and ... More
Time reversibility and nonequilibrium thermodynamics of second-order stochastic processesSep 14 2012Oct 27 2015Nonequilibrium thermodynamics of a general second-order stochastic system is investigated. We prove that at steady state, under inversion of velocities, the condition of time-reversibility over the phase space is equivalent to the antisymmetry of spatial ... More
Improved Accent Classification Combining Phonetic Vowels with Acoustic FeaturesFeb 24 2016Researches have shown accent classification can be improved by integrating semantic information into pure acoustic approach. In this work, we combine phonetic knowledge, such as vowels, with enhanced acoustic features to build an improved accent classification ... More
Normalization of quasinormal modes in leaky optical cavities and plasmonic resonatorsJan 23 2015Dec 02 2015We discuss three formally different formulas for normalization of quasinormal modes currently in use for modeling optical cavities and plasmonic resonators and show that they are complementary and provide the same result. Regardless of the formula used ... More
Competing with the Empirical Risk Minimizer in a Single PassDec 20 2014Feb 25 2015In many estimation problems, e.g. linear and logistic regression, we wish to minimize an unknown objective given only unbiased samples of the objective function. Furthermore, we aim to achieve this using as few samples as possible. In the absence of computational ... More
On the Local Minima of the Empirical RiskMar 25 2018Oct 17 2018Population risk is always of primary interest in machine learning; however, learning algorithms only have access to the empirical risk. Even for applications with nonconvex nonsmooth losses (such as modern deep networks), the population risk is generally ... More
On the ability of neural nets to express distributionsFeb 22 2017Jun 02 2017Deep neural nets have caused a revolution in many classification tasks. A related ongoing revolution---also theoretically not understood---concerns their ability to serve as generative models for complicated types of data such as images and texts. These ... More
Computing a Nonnegative Matrix Factorization -- ProvablyNov 03 2011In the Nonnegative Matrix Factorization (NMF) problem we are given an $n \times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express $M$ as $A W$ where $A$ and $W$ are nonnegative matrices of size $n \times r$ and $r \times m$ respectively. ... More
Provable Bounds for Learning Some Deep RepresentationsOct 23 2013We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others. Our generative model is an $n$ node multilayer neural net that has degree at most $n^{\gamma}$ for some $\gamma ... More
Learning Two-layer Neural Networks with Symmetric InputsOct 16 2018Feb 03 2019We give a new algorithm for learning a two-layer neural network under a general class of input distributions. Assuming there is a ground-truth two-layer network $$ y = A \sigma(Wx) + \xi, $$ where $A,W$ are weight matrices, $\xi$ represents noise, and ... More
Stabilized SVRG: Simple Variance Reduction for Nonconvex OptimizationMay 01 2019Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective. For nonconvex objectives, these techniques can also find a first-order stationary point (with small gradient). However, in nonconvex ... More
AI Coding: Learning to Construct Error Correction CodesJan 17 2019In this paper, we investigate an artificial-intelligence (AI) driven approach to design error correction codes (ECC). Classic error correction code was designed upon coding theory that typically defines code properties (e.g., hamming distance, subchannel ... More
Global Convergence of Policy Gradient Methods for the Linear Quadratic RegulatorJan 15 2018Oct 21 2018Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, ... More
A Tensor Approach to Learning Mixed Membership Community ModelsFeb 12 2013Oct 24 2013Community detection is the task of detecting hidden communities from observed interactions. Guaranteed community detection has so far been mostly limited to models with non-overlapping communities such as the stochastic block model. In this paper, we ... More
Generalization and Equilibrium in Generative Adversarial Nets (GANs)Mar 02 2017Aug 01 2017We show that training of generative adversarial network (GAN) may not have good generalization properties; e.g., training may appear successful but the trained distribution may be far from target distribution in standard metrics. However, generalization ... More
Provable learning of Noisy-or NetworksDec 28 2016Many machine learning applications use latent variable models to explain structure in data, whereby visible variables (= coordinates of the given datapoint) are explained as a probabilistic function of some hidden variables. Finding parameters with the ... More
More Algorithms for Provable Dictionary LearningJan 03 2014In dictionary learning, also known as sparse coding, the algorithm is given samples of the form $y = Ax$ where $x\in \mathbb{R}^m$ is an unknown random sparse vector and $A$ is an unknown dictionary matrix in $\mathbb{R}^{n\times m}$ (usually $m > n$, ... More
Spin dynamics in the XY modelOct 18 2010Oct 21 2010We study the evolution of entanglement, quantum correlation and classical correlation for the one dimensional XY model in external transverse magnetic field. The system is initialized in the full polarized state along the z axis, after annealing, different ... More
Homotopy Analysis for Tensor PCAOct 28 2016Jun 14 2017Developing efficient and guaranteed nonconvex algorithms has been an important challenge in modern machine learning. Algorithms with good empirical performance such as stochastic gradient descent often lack theoretical guarantees. In this paper, we analyze ... More
Minimal Realization Problems for Hidden Markov ModelsNov 13 2014Dec 14 2015Consider a stationary discrete random process with alphabet size d, which is assumed to be the output process of an unknown stationary Hidden Markov Model (HMM). Given the joint probabilities of finite length strings of the process, we are interested ... More
Provable Algorithms for Inference in Topic ModelsMay 27 2016Recently, there has been considerable progress on designing algorithms with provable guarantees -- typically using linear algebraic methods -- for parameter learning in latent variable models. But designing provable algorithms for inference has proven ... More
Common Origin of Soft mu-tau and CP Breaking in Neutrino Seesaw and the Origin of MatterJan 06 2010Apr 01 2010Neutrino oscillation data strongly support mu-tau symmetry as a good approximate flavor symmetry of the neutrino sector, which has to appear in any viable theory for neutrino mass-generation. The mu-tau breaking is not only small, but also the source ... More
Quasi-normal mode approach to the local-field problem in quantum opticsJan 23 2015Apr 13 2015The local-field (LF) problem of a finite-size dipole emit- ter radiating inside a lossy inhomogeneous structure is a long-standing and challenging quantum optical problem, and it now is becoming more important due to rapid advances in solid-state fabrication ... More
DynIMS: A Dynamic Memory Controller for In-memory Storage on HPC SystemsSep 29 2016In order to boost the performance of data-intensive computing on HPC systems, in-memory computing frameworks, such as Apache Spark and Flink, use local DRAM for data storage. Optimizing the memory allocation to data storage is critical to delivering performance ... More
Simple, Efficient, and Neural Algorithms for Sparse CodingMar 02 2015Sparse coding is a basic task in many fields including signal processing, neuroscience and machine learning where the goal is to learn a basis that enables a sparse representation of a given set of data, if one exists. Its standard formulation is as a ... More
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and AutoencodersJun 23 2012Nov 12 2012We present a new algorithm for Independent Component Analysis (ICA) which has provable performance guarantees. In particular, suppose we are given samples of the form $y = Ax + \eta$ where $A$ is an unknown $n \times n$ matrix and $x$ is a random variable ... More
Finding Overlapping Communities in Social Networks: Toward a Rigorous ApproachDec 08 2011A "community" in a social network is usually understood to be a group of nodes more densely connected with each other than with the rest of the network. This is an important concept in most domains where networks arise: social, technological, biological, ... More
Efficient Quantum RatchetJun 16 2012Quantum resonance is one of the main characteristics of the quantum kicked rotor, which has been used to induce accelerated ratchet current of the particles with a generalized asymmetry potential. Here we show that by desynchronizing the kicked potentials ... More
Homotopy Analysis for Tensor PCAOct 28 2016Nov 02 2016Developing efficient and guaranteed nonconvex algorithms has been an important challenge in modern machine learning. Algorithms with good empirical performance such as stochastic gradient descent often lack theoretical guarantees. In this paper, we analyze ... More
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimizationJun 24 2015We develop a family of accelerated stochastic algorithms that minimize sums of convex functions. Our algorithms improve upon the fastest running time for empirical risk minimization (ERM), and in particular linear least-squares regression, across a wide ... More
Stronger generalization bounds for deep nets via a compression approachFeb 14 2018Nov 26 2018Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but do not as yet result in sample complexity bounds better than naive parameter ... More
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor DecompositionMar 06 2015We analyze stochastic gradient descent for optimizing non-convex functions. In many cases for non-convex functions the goal is to find a reasonable local minimum, and the main concern is that gradient updates are trapped in saddle points. In this paper ... More
Online Service with DelayAug 18 2017In this paper, we introduce the online service with delay problem. In this problem, there are $n$ points in a metric space that issue service requests over time, and a server that serves these requests. The goal is to minimize the sum of distance traveled ... More
On the canonical maps of nonsingular threefolds of general typeDec 21 2016Dec 28 2016Let $S$ be a nonsingular minimal complex projective surface of general type and the canonical map of $S$ is generically finite. Beauville showed that the geometric genus of the image of the canonical map is vanishing or equals the geometric genus of $S$ ... More
Smooth solution to higher dimensional complex Plateau problemDec 07 2017Dec 14 2017Let $X$ be a compact connected strongly pseudoconvex $CR$ manifold of real dimension $2n-1$ in $\mathbb{C}^{N}$. For $n\ge 3$, Yau solved the complex Plateau problem of hypersurface type by checking a bunch of Kohn-Rossi cohomology groups in 1981. In ... More
The Briot-Bouquet systems and the center families for holomorphic dynamical systemsJul 15 2013We give a complete solution to the existence of isochronous center families for holomorphic dynamical systems. The study of center families for n-dimensional holomorphic dynamical systems naturally leads to the study of (n-1)-dimensional Briot-Bouquet ... More
Dynamics of bilayer membranes. 1. Isolated membraneOct 24 1997By adopting the approximations suggested in the theory of thin shells, the hydrodynamic theory of liquid crystals established by Eriksen [Arch. Rational Mech. Anal. 4 (1960) 231; Trans. Soc. Rheol. 4 (1960) 29] and Leslie [Adv. Liq. Cryst. 4 (1979) 1] ... More
Stochastic Optimization of Smooth LossNov 30 2013In this paper, we first prove a high probability bound rather than an expectation bound for stochastic optimization with smooth loss. Furthermore, the existing analysis requires the knowledge of optimal classifier for tuning the step size in order to ... More
Recent Experimental Results on Leptonic $D^+_{(s)}$ Decays, Semileptonic $D$ Decays and Extraction of $|V_{cd(s)}|$Nov 14 2014The recent experimental results on leptonic $D^+_{(s)}$ decays, semileptonic $D$ decays, determinations of decay constants and form factors, as well as extractions of $|V_{cd}|$ and $|V_{cs}|$ are briefly reviewed. Global analysis of all existing measurements ... More
Programmatic Control of a Compiler for Generating High-performance Spatial HardwareNov 21 2017Dec 13 2017This methodology paper addresses high-performance high-productivity programming on spatial architectures. Spatial architectures are efficient for executing dataflow algorithms, yet for high-performance programming, the productivity is low and verification ... More
Poisson Subsampling Algorithms for Large Sample Linear Regression in Massive DataSep 07 2015Nov 23 2015Large sample size brings the computation bottleneck for modern data analysis. Subsampling is one of efficient strategies to handle this problem. In previous studies, researchers make more fo- cus on subsampling with replacement (SSR) than on subsampling ... More
Review and Report on Results of Leptonic Decays of $D^+$ and $D^+_s$ MesonsSep 01 2012In the last 25 years, many $e^+e^-$ experiments and fixed-target experiments performed to search for and study the leptonic decays of the $D^+$ and $D^+_s$ mesons. By 2012, more than 530 signal events of the $D^+$ leptonic decays and about $4\times10^3$ ... More
Transport through the single-molecular dots in an external irradiationMar 18 2003Apr 03 2003We present a fully nonequilibrium calculation of the low-temperature transport properties of a single molecular quantum dot coupled to local phonon mode when an ac field is applied to the gate. The resonant behavior is shown in the time-averaged differential ... More
Expressing Sparse Matrix Computations for Productive Performance on Spatial ArchitecturesOct 12 2018This paper addresses spatial programming of sparse matrix computations for productive performance. The challenge is how to express an irregular computation and its optimizations in a regular way. A sparse matrix has (non-zero) values and a structure. ... More
Productively Expressing High-performance Spatial Designs of Givens Rotation-based QR Decomposition AlgorithmMay 19 2018QR decomposition is used prevalently in wireless communication. In this paper, we express the Givens-rotation-based QR decomposition algorithm on a spatial architecture using T2S (Temporal To Spatial), a high-productivity spatial programming methodology ... More
Recent Bes Results on psi(3770) and D Meson Production and DecayJun 10 2004Using a data sample of 17.7 $\rm pb^{-1}$ collected at 3.773 GeV with the BES-II detector at the BEPC, the cross sections for $D^0 \bar D^{0}$ and $D^+D^-$ productions at 3.773 GeV have been measured. From the data sample about 33 $\rm pb^{-1}$ taken ... More
Supervisor Synthesis to Thwart Cyber Attack with Bounded Sensor Reading AlterationsAug 14 2016Aug 24 2016One of the major challenges about cyber physical systems is how to prevent cyber attacks to ensure system integrity. There has been a large number of different types of attacks discussed in the modern control and computer science communities. In this ... More
Current and Shot Noise in a Quantum Dot Coupled to Ferromagnetic Leads in the Large U LimitOct 16 2002Using the Keldysh nonequilibrium Green function technique, we study the current and shot noise spectroscopy of a single interacting quantum dot coupled to two ferromagnetic leads with different polarizations. The polarizations of leads can be both parallel ... More
PP-wave String Interactions from String Bit ModelAug 30 2002Nov 27 2002We construct the string states $|O_{p}^J>_J$, $|O_{q}^{J_1}>_{{J_1}{J_2}}$ and $|O_{0}^{J_{1}J_{2}}>_{{J_1}{J_2}}$ in the Hilbert space of the quantum mechanical orbifold model so as to calculate the three point functions and the matrix elements of the ... More
Perturbative and non-perturbative parts of eigenstates and local spectral density of states: the Wigner band random matrix modelDec 07 1998A generalization of Brillouin-Wigner perturbation theory is applied numerically to the Wigner Band Random Matrix model. The perturbation theory tells that a perturbed energy eigenstate can be divided into a perturbative part and a non-perturbative part ... More
X-ray Fluorescence SectioningOct 26 2012In this paper, we propose an x-ray fluorescence imaging system for elemental analysis. The key idea is what we call "x-ray fluorescence sectioning". Specifically, a slit collimator in front of an x-ray tube is used to shape x-rays into a fan-beam to illuminate ... More
Comment on "Atomic Scale Structure and Chemical Composition across Order-Disorder Interfaces"Feb 17 2011Interfaces have long been known to be the key to many mechanical and electric properties. To nickel base superalloys which have perfect creep and fatigue properties and have been widely used as materials of turbine blades, interfaces determine the strengthening ... More
A New Class of Solvable Many-Body ProblemsOct 02 2012A new class of solvable $N$-body problems is identified. They describe $N$ unit-mass point particles whose time-evolution, generally taking place in the complex plane, is characterized by Newtonian equations of motion "of goldfish type" (acceleration ... More
Molecular Polarizability of Water from the Local Dielectric Response TheoryJun 04 2017Jul 26 2017We propose a fully ab initio theory to compute the electron density response under the perturbation in the local field. This method is based on our recently developed local dielectric response theory [Phys. Rev. B 92, 241107(R), 2015], which provides ... More
Notes on shear viscosity bound violation in anisotropic modelsOct 23 2015Nov 29 2015The shear viscosity bound violation in Einstein gravity for anisotropic black branes is discussed, with the aim of constraining the deviation of the shear viscosity-entropy density ratio from the shear viscosity bound using causality and thermodynamics ... More
Integrable one-dimensional supersymmetric q-deformed extended Hubbard model with boundary Kondo impuritySep 17 1999Dec 10 1999This paper has been withdrawn by the author, due to an error in relation (11).