Y. K. Wu - Department of Engineering Physics, Tsinghua Univeristy, Beijing 100084, China

Y. K. Wu
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Name
Y. K. Wu
Affiliation
Department of Engineering Physics, Tsinghua Univeristy, Beijing 100084, China
City
Beijing
Country
China

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Computer Science - Computer Vision and Pattern Recognition (6)
 
Physics - Materials Science (5)
 
Mathematics - Information Theory (4)
 
Computer Science - Information Theory (4)
 
Astrophysics of Galaxies (4)
 
Solar and Stellar Astrophysics (3)
 
Computer Science - Computation and Language (3)
 
Mathematics - Analysis of PDEs (3)
 
Computer Science - Learning (3)
 
High Energy Physics - Theory (3)
 
High Energy Physics - Phenomenology (3)
 
Statistics - Machine Learning (2)
 
Physics - Plasma Physics (2)
 
Computer Science - Artificial Intelligence (2)
 
Physics - Strongly Correlated Electrons (1)
 
Physics - Statistical Mechanics (1)
 
High Energy Physics - Lattice (1)
 
Computer Science - Networking and Internet Architecture (1)
 
Computer Science - Cryptography and Security (1)
 
Physics - Physics and Society (1)
 
Mathematics - Algebraic Geometry (1)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (1)
 
Nonlinear Sciences - Chaotic Dynamics (1)
 
Computer Science - Human-Computer Interaction (1)
 
High Energy Physics - Experiment (1)
 
Quantum Physics (1)
 
Statistics - Methodology (1)
 
Physics - Accelerator Physics (1)
 
General Relativity and Quantum Cosmology (1)
 
Physics - Optics (1)

Publications Authored By Y. K. Wu

Motivated from flavor non-universality and anomalies in semi-leptonic B-meson decays, we present a general and systematic discussion about how to construct anomaly-free $U(1)'$ gauge theories based on an extended standard model with only three right-handed neutrinos. If all fermions are vector-like under this new gauge symmetry, the most general family non-universal charge assignments, $(a,b,c)$ for three-generation quarks and $(d,e,f)$ for leptons, need to satisfy just one condition to be anomaly-free, $3(a+b+c)=-(d+e+f)$. Any assignment can be linear combinations of five independent anomaly-free solutions. Read More

Characterization and certification of nonlocal correlations is one of the the central topics in quantum information theory. In this work, we develop the detection methods of entanglement and steering based on the universal uncertainty relations and fine-grained uncertainty relations. In the course of our study, the uncertainty relations are formulated in majorization form, and the uncertainty quantifier can be chosen as any convex Schur concave functions, this leads to a large set of inequalities, including all existing criteria based on entropies. Read More

Spreading truths is recognized as a feasible strategy for inhibiting rumors. This paper is devoted to assessing the effectiveness of the truth-spreading strategy. An individual-level rumor-truth spreading model (the generic URTU model) is derived. Read More

High-order harmonic generation (HHG) in isolated atoms and molecules has been widely utilized in extreme ultraviolet (XUV) photonics and attosecond pulse metrology. Recently, HHG has also been observed in solids, which could lead to important applications such as all-optical methods to image valance charge density and reconstruction of electronic band structures, as well as compact XUV light sources. Previous HHG studies are confined on crystalline solids; therefore decoupling the respective roles of long-range periodicity and high density has been challenging. Read More

A new gas puff imaging (GPI) diagnostic has been developed on HL-2A tokamak to study two-dimensional plasma edge turbulence in poloidal vs radial plane. During a discharge, neutral helium or deuterium gas is puffed at the edge of the plasma through a rectangular multi-capillary nozzle to generate a gas cloud on the observing plane. Then a specially designed telescope and a high-speed camera are used to observe and photograph the emission from the neutral gas cloud. Read More

The extraction system of CSNS mainly consists of two kinds of magnets: eight kickers and one lambertson magnet. In this paper, firstly, the magnetic test results of the eight kickers were introduced and then the filed uniformity and magnetizing relationship of the kickers were given. Secondly, during the beam commissioning in the future, in order to obtain more accurate magnetizing relationship, a new method to measure the magnetizing coefficients of the kickers by the real extraction beam was given and the data analysis would also be processed. Read More

The massive evolved star Eta Carinae is the most luminous star in the Milky Way and has the highest steady wind mass-loss rate of any known star. Radiative transfer models of the spectrum by Hillier et al. predict that H-alpha is mostly emitted in regions of the wind at radii of 6 to 60 AU from the star (2. Read More

Networks are a useful representation for data on connections between units of interests, but the observed connections are often noisy and/or include missing values. One common approach to network analysis is to treat the network as a realization from a random graph model, and estimate the underlying edge probability matrix, which is sometimes referred to as network denoising. Here we propose a generalized linear model with low rank effects to model network edges. Read More

A unified field theory of all known basic forces and elementary particles is built based on a postulate of gauge invariance and coordinate independence along with a conformal scaling gauge symmetry. The hyper-spin charge of a unified hyper-spinor field is conjectured to correlate to the dimension of a hyper-spacetime with $D_h=19$ via a maximal symmetry. A unified fundamental interaction is postulated to be governed by a hyper-spin gauge symmetry SP(1,$D_h$-1). Read More

Camera parameters not only play an important role in determining the visual quality of perceived images, but also affect the performance of vision algorithms, for a vision-guided robot. By quantitatively evaluating four object detection algorithms, with respect to varying ambient illumination, shutter speed and voltage gain, it is observed that the performance of the algorithms is highly dependent on these variables. From this observation, a novel active control of camera parameters method is proposed, to make robot vision more robust under different light conditions. Read More

Massive public resume data emerging on the WWW indicates individual-related characteristics in terms of profile and career experiences. Resume Analysis (RA) provides opportunities for many applications, such as talent seeking and evaluation. Existing RA studies based on statistical analyzing have primarily focused on talent recruitment by identifying explicit attributes. Read More

Spatial distributions of other cell interference (OCIF) and interference to own-cell power ratio (IOPR) with reference to the distance between a mobile and its serving base station (BS) are modeled for the down-link reception of cellular systems based on the best-cell configuration instead of the nearest-cell configuration. This enables a more realistic evaluation of two competing objectives in network dimensioning: coverage and rate capacity. More outcomes useful for dynamic network dimensioning are also derived, including maximum BS transmission power per cell size and the cell density required for an adequate coverage of a given traffic density. Read More

Stellar feedback from high-mass stars can strongly influence the surrounding interstellar medium and regulate star formation. Our new ALMA observations reveal sequential high-mass star formation taking place within one sub-virial filamentary clump (the G9.62 clump) in the G9. Read More

Recent experimental realizations of superfluid mixtures of Bose and Fermi quantum gases provide a unique platform for exploring diverse superfluid phenomena. We study dipole oscillations of a double superfluid in a cigar-shaped optical dipole trap, consisting of $^{41}$K and $^{6}$Li atoms with a large mass imbalance, where the oscillations of the bosonic and fermionic components are coupled via the Bose-Fermi interaction. In our high-precision measurements, the frequencies of both components are observed to be shifted from the single-species ones, and exhibit unusual features. Read More

In this paper, we consider the following nonlinear Klein-Gordon equation \begin{align*} \partial_{tt}u-\Delta u+u=|u|^{p-1}u,\qquad t\in \mathbb{R},\ x\in \mathbb{R}^d, \end{align*} with $1Read More

We present here a model of carrier distribution and transport in semiconductor alloys accounting for quantum localization effects in disordered materials. This model is based on the recent development of a mathematical theory of quantum localization which introduces for each type of carrier a spatial function called \emph{localization landscape}. These landscapes allow us to predict the localization regions of electron and hole quantum states, their corresponding energies, and the local densities of states. Read More

We have carried out bulk-sensitive hard x-ray photoelectron spectroscopy (HAXPES) measurements on in situ cleaved and ex-situ polished SmB6 single crystals. Using the multiplet-structure in the Sm 3d core level spectra, we obtained reliably the valence number of Sm in bulk SmB6 to be close to 2.54 at ~5 K. Read More

Urbach tails in semiconductors are often associated to effects of compositional disorder. The Urbach tail observed in InGaN alloy quantum wells of solar cells and LEDs by biased photocurrent spectroscopy is shown to be characteristic of the ternary alloy disorder. The broadening of the absorption edge observed for quantum wells emitting from violet to green (indium content ranging from 0 to 28\%) corresponds to a typical Urbach energy of 20~meV. Read More

We give a counterexample to the vector generalization of Costa's entropy power inequality (EPI) due to Liu, Liu, Poor and Shamai. In particular, the claimed inequality can fail if the matix-valued parameter in the convex combination does not commute with the covariance of the additive Gaussian noise. Conversely, the inequality holds if these two matrices commute. Read More

Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the \textit{analogical} properties of the embedded entities and relations. Read More

Using the three-dimensional three-state Potts model, which has the same Z(3) global symmetry as that of the QCD system, we study the sign distribution of generalized susceptibilities in the whole phase plane, and the fluctuations of generalized susceptibilities nearby the phase transition line. The sign change and non-monotonic fluctuations are observable in a small area nearby the phase transition line. A bit further away from the phase transition line, the sign of odd-order susceptibility is opposite in the symmetry (disorder) and broken (order) phases, and that of the even-order one is the same positive in the two phases. Read More

Edge shear flow and its effect on regulating turbulent transport have long been suspected to play an important role in plasmas operating near the Greenwald limit $ n_G $. In this study, equilibrium profiles as well as the turbulent particle flux and Reynolds stress across the separatrix in the HL-2A tokamak are examined as $ n_G $ is approached in ohmic L-mode discharges. As the normalized line-averaged density $ \bar{n}_e/n_G $ is raised, the electron density and pressure gradient increase, and the electron temperature flattens. Read More

This paper addresses the discount pricing in word-of-mouth (WOM) marketing. A new discount strategy known as the Infection-Based Discount (IBD) strategy is proposed. The basic idea of the IBD strategy lies in that each customer enjoys a discount that is linearly proportional to his/her influence in the WOM network. Read More

As compared to the traditional advertising, the word-of-mouth (WOM) communications have striking advantages such as significantly lower cost and rapid delivery; this is especially the case with the popularity of online social networks. This paper addresses the issue of maximizing the overall profit of a WOM marketing campaign. A marketing process with both positive and negative WOM is modeled as a dynamical model knwn as the SIPNS model, and the profit maximization problem is modeled as a constrained optimization problem. Read More

Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. This paper presents a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Read More

For $n\ge5$, it is well known that the moduli space $\mathfrak{M_{0,\:n}}$ of unordered $n$ points on the Riemann sphere is a quotient space of the Zariski open set $K_n$ of $\mathbb C^{n-3}$ by an $S_n$ action. The stabilizers of this $S_n$ action at certain points of this Zariski open set $K_n$ correspond to the groups fixing the sets of $n$ points on the Riemann sphere. Let $\alpha$ be a subset of $n$ distinct points on the Riemann sphere. Read More

Finding accurate reduced descriptions for large, complex, dynamically evolving networks is a crucial enabler to their simulation, analysis, and, ultimately, design. Here we propose and illustrate a systematic and powerful approach to obtaining good collective coarse-grained observables-- variables successfully summarizing the detailed state of such networks. Finding such variables can naturally lead to successful reduced dynamic models for the networks. Read More

Multiple antennas have been exploited for spatial multiplexing and diversity transmission in a wide range of communication applications. However, most of the advances in the design of high speed wireless multiple-input multiple output (MIMO) systems are based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. Although the Gaussian signal is capacity-achieving, signals conforming to discrete constellations are transmitted in practical communication systems. Read More

In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In particular, a greedy sorting strategy is proposed to rearrange the order by which the members are included into the integration process. Read More

In this paper, we investigate secure transmission over the large-scale multiple-antenna wiretap channel with finite alphabet inputs. First, we investigate the case where instantaneous channel state information (CSI) of the eavesdropper is known at the transmitter. We show analytically that a generalized singular value decomposition (GSVD) based design, which is optimal for Gaussian inputs, may exhibit a severe performance loss for finite alphabet inputs in the high signal-to-noise ratio (SNR) regime. Read More

Since the matrix formed by nonlocal similar patches in a natural image is of low rank, the nuclear norm minimization (NNM) has been widely used for image restoration. However, NNM tends to over-shrink the rank components and treats the different rank components equally, thus limits its capability and flexibility. This paper proposes a new approach for image restoration based ADMM framework via non-convex weighted Schatten $p$-norm minimization (WSNM). Read More

Compressive sensing (CS) has attracted considerable research from signal/image processing communities. Recent studies further show that structured or group sparsity often leads to more powerful signal reconstruction techniques in various CS taskes. Unlike the conventional sparsity-promoting convex regularization methods, this paper proposes a new approach for image compressive sensing recovery using group sparse coding via non-convex weighted $\ell_p$ minimization. Read More

In this paper, we propose a learning-based low-overhead channel estimation method for coordinated beamforming in ultra-dense networks. We first show through simulation that the channel state information (CSI) of geographically separated base stations (BSs) exhibits strong non-linear correlations in terms of mutual information. This finding enables us to adopt a novel learning-based approach to remotely infer the quality of different beamforming patterns at a dense-layer BS based on the CSI of an umbrella control-layer BS. Read More

We have discovered an incommensurate spin density wave (SDW) state in Fe$_3$Ga$_4$ that is driven by an instability in its ferromagnetic (FM) state via a combination of single crystal neutron diffraction and first principles calculation. Fe$_3$Ga$_4$ displays a complex sequence of magnetic behaviors including a FM ground state, an antiferromagnetic intermediate state, and an unusual return to ferromagnetism at high temperature. Anomalies in the charge transport along with electronic structure calculations indicating Fermi surface nesting only in the FM majority band lead us to conclude that it is the FM state that is unstable to the formation of a SDW. Read More

We use high resolution angle-resolved photoemission spectroscopy (ARPES) and electronic structure calculations to study the electronic properties of rare-earth monoantimonides RSb (R = Y, Ce, Gd, Dy, Ho, Tm, Lu). The experimentally measured Fermi surface (FS) of RSb consists of at least two concentric hole pockets at the $\Gamma$ point and two intersecting electron pockets at the $X$ point. These data agree relatively well with the electronic structure calculations. Read More

Let $G=(V,E)$ be a locally finite connected weighted graph, $\Delta$ be the usual graph Laplacian. In this paper, we study the blow-up problems for the nonlinear parabolic equation $u_t=\Delta u + f(u)$ on $G$. The blow-up phenomenons of the equation are discussed in terms of two cases: (i) an initial condition is given; (ii) a Dirichlet boundary condition is given. Read More

This paper introduces a novel method to account for quantum disorder effects into the classical drift-diffusion model of semiconductor transport through the localization landscape theory. Quantum confinement and quantum tunneling in the disordered system change dramatically the energy barriers acting on the perpendicular transport of heterostructures. In addition they lead to percolative transport through paths of minimal energy in the 2D landscape of disordered energies of multiple 2D quantum wells. Read More

We investigate loop corrections to the primordial fluctuations in the single-field inflationary paradigm from spectator fields that experience a transition of their vacuum expectation values. This phase transition involves a classical evolution effectively driven by a negative mass term from the potential where field perturbations can grow on superhorizon scales. We show that important corrections to the curvature perturbation can be generated by field perturbations that are frozen outside the horizon by the time of the growing phase, yet the correction to tensor perturbation is naturally suppressed by the spatial derivative couplings between spectator fields and graviton. Read More

We present stellar density maps of the Galactic outer disc with red clump stars from the LAMOST data. These samples are separated into younger (mean age ~ 2.7 Gyr) and older (mean age ~ 4. Read More

Quantitative understanding of relationships between students' behavioral patterns and academic performance is a significant step towards personalized education. In contrast to previous studies that mainly based on questionnaire surveys, in this paper, we collect behavioral records from 18,960 undergraduate students' smart cards and propose a novel metric, called \emph{orderness}, which measures the regularity of campus daily life (e.g. Read More

Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local measurements/observations are scattered over a wide geographical area. However, the convergence of Gaus- sian BP is still an open issue. In this paper, we consider the convergence of Gaussian BP, focusing in particular on the convergence of the information matrix. Read More

Given a convolutional neural network (CNN) that is pre-trained for object classification, this paper proposes to use active question-answering to semanticize neural patterns in conv-layers of the CNN and mine part concepts. For each part concept, we mine neural patterns in the pre-trained CNN, which are related to the target part, and use these patterns to construct an And-Or graph (AOG) to represent a four-layer semantic hierarchy of the part. As an interpretable model, the AOG associates different CNN units with different explicit object parts. Read More

Voice is envisioned to be a popular way for humans to interact with Internet-of-Things (IoT) devices. We propose a proximity-based user authentication method (called PIANO) for access control on such voice-powered IoT devices. PIANO leverages the built-in speaker, microphone, and Bluetooth that voice-powered IoT devices often already have. Read More

We present a CO(2-1) mosaic map of the spiral galaxy NGC 6946 by combining data from the Submillimeter Array and the IRAM 30 m telescope. We identify 390 giant molecular clouds (GMCs) from the nucleus to 4.5 kpc in the disk. Read More

Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, LSSC. In the past, convex optimization with sparsity-promoting convex regularization was usually regarded as a standard scheme for estimating sparse signals in noise. However, using convex regularization can not still obtain the correct sparsity solution under some practical problems including image inverse problems. Read More

Facial landmark localization is a fundamental module for face recognition. Current common approach for facial landmark detection is cascaded regression, which is composed by two steps: feature extraction and facial shape regression. Recent methods employ deep convolutional networks to extract robust features in each step and the whole system could be regarded as a deep cascaded regression architecture. Read More

With electron and hole pockets touching at the Weyl node, type-II Weyl semimetal is a newly proposed topological state distinct from its type-I cousin. We numerically study the localization effect for tilted type-I as well as type-II Weyl semimetals and give the global phase diagram. For dis- ordered type-I Weyl semimetal, an intermediate three-dimensional quantum anomalous Hall phase is confirmed between Weyl semimetal phase and diffusive metal phase. Read More

In this paper, we present a rigorous study of the direct scattering problem that arises from the complete integrability of the Benjamin--Ono (BO) equation. In particular, we establish existence, uniqueness, and asymptotic properties of the Jost solutions to the scattering operator in the Fokas--Ablowitz inverse scattering transform (IST). Formulas relating different scattering coefficients are proven, together with their asymptotic behavior with respect to the spectral parameter. Read More

Building a voice conversion (VC) system from non-parallel speech corpora is challenging but highly valuable in real application scenarios. In most situations, the source and the target speakers do not repeat the same texts or they may even speak different languages. In this case, one possible, although indirect, solution is to build a generative model for speech. Read More