Yang Wang - Department of Chemistry, University of Southern California, Los Angeles, CA

Yang Wang
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Name
Yang Wang
Affiliation
Department of Chemistry, University of Southern California, Los Angeles, CA
City
Los Angeles
Country
United States

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Quantum Physics (7)
 
Computer Science - Learning (5)
 
Computer Science - Computer Vision and Pattern Recognition (5)
 
Mathematics - Functional Analysis (4)
 
Statistics - Machine Learning (4)
 
Physics - Materials Science (4)
 
Computer Science - Information Theory (4)
 
Mathematics - Information Theory (4)
 
Computer Science - Artificial Intelligence (3)
 
Physics - Superconductivity (3)
 
Physics - Mesoscopic Systems and Quantum Hall Effect (3)
 
Mathematics - Dynamical Systems (2)
 
Physics - Atmospheric and Oceanic Physics (2)
 
Physics - Strongly Correlated Electrons (2)
 
Physics - Physics and Society (2)
 
Computer Science - Computation and Language (2)
 
Computer Science - Software Engineering (2)
 
Computer Science - Distributed; Parallel; and Cluster Computing (2)
 
Computer Science - Neural and Evolutionary Computing (1)
 
Solar and Stellar Astrophysics (1)
 
Physics - Atomic Physics (1)
 
Physics - Plasma Physics (1)
 
Physics - Statistical Mechanics (1)
 
Computer Science - Human-Computer Interaction (1)
 
Computer Science - Cryptography and Security (1)
 
Computer Science - Information Retrieval (1)
 
Mathematics - Numerical Analysis (1)
 
Cosmology and Nongalactic Astrophysics (1)
 
Mathematics - Optimization and Control (1)
 
Physics - Other (1)

Publications Authored By Yang Wang

In this paper, we first use the super-sub solution method to prove the local exponential asymptotic stability of some entire solutions to reaction diffusion equations, including the bistable and monostable cases. In the bistable case, we not only obtain the similar asymptotic stability result given by Yagisita in 2003, but also simplify his proof. For the monostable case, it is the first time to discuss the local asymptotic stability of entire solutions. Read More

In this paper we are concerned with the entire solutions for the classical competitive Lotka-Volterra system with diffusion in the weak competition. For this purpose we firstly analyze the asymptotic behavior of traveling front solutions for this system connecting the origin and the positive equilibrium. Then, by using two different ways to construct pairs of coupled super-sub solutions of this system, we obtain two different kinds of entire solutions. Read More

Electrons in two-dimensional graphene sheets behave as interacting chiral Dirac fermions and have unique screening properties due to their symmetry and reduced dimensionality. By using a combination of scanning tunneling spectroscopy (STM/STS) measurements and theoretical modeling we have characterized how graphene's massless charge carriers screen individual charged calcium atoms. A back-gated graphene device configuration has allowed us to directly visualize how the screening length for this system can be tuned with carrier density. Read More

It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory-augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. Read More

Single-Radio-Frequency (RF) Multiple-Input-Multiple-Output (MIMO) systems such as the spatial modulation (SM) system and the space shift keying (SSK) system have been proposed to pursue a high spectral efficiency while keeping a low cost and complexity transceiver design. Currently, polarization domain resource has been introduced to the single-RF MIMO system to reduce the size of the transmit antenna array and provide 1 bit per channel use (bpcu) multiplexing gain. Nevertheless, the polarization domain resource still has the potential to provide a higher multiplexing gain in the polarized single-RF MIMO system. Read More

The proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diffusion is known as \textit{early rumor detection}, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. Thus, identifying trending rumors demands an efficient yet flexible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. Read More

In this paper, we develop a novel paradigm, namely hypergraph shift, to find robust graph modes by probabilistic voting strategy, which are semantically sound besides the self-cohesiveness requirement in forming graph modes. Unlike the existing techniques to seek graph modes by shifting vertices based on pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting high-order edges (hyperedges) to deliver graph modes. Read More

Agile techniques recently have received attention in developing safety-critical systems. However, a lack of empirical knowledge of performing safety assurance techniques in practice, especially safety analysis into agile development processes prevents further steps. In this article, we aim at investigating the feasibility and the effects of our S-Scrum development process, and stepwise improving and proposing an Optimized S-Scrum development process for safety-critical systems in a real environment. Read More

We consider the problem of semantic image segmentation using deep convolutional neural networks. We propose a novel network architecture called the label refinement network that predicts segmentation labels in a coarse-to-fine fashion at several resolutions. The segmentation labels at a coarse resolution are used together with convolutional features to obtain finer resolution segmentation labels. Read More

Given a pedestrian image as a query, the purpose of person re-identification is to identify the correct match from a large collection of gallery images depicting the same person captured by disjoint camera views. The critical challenge is how to construct a robust yet discriminative feature representation to capture the compounded variations in pedestrian appearance. To this end, deep learning methods have been proposed to extract hierarchical features against extreme variability of appearance. Read More

Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve state-of-the-art human action recognition results on a number of datasets. This paper improves their performance by applying rank pooling to each trajectory, encoding the temporal evolution of deep learning features computed along the trajectory. This leads to Evolution-Preserving Trajectory (EPT) descriptors, a novel type of video descriptor that significantly outperforms Trajectory-pooled Deep-learning Descriptors. Read More

Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and/or angles, and may subsequently yield very different results. In fact, dealing with the landmarks with \illshapes caused by the photography of q-users is often nontrivial and has seldom been studied. Read More

The Green function plays an essential role in the Kohn-Korringa-Rostocker (KKR) multiple scattering method. In practice, it is constructed from the regular and irregular solutions of the local Kohn-Sham equation and robust methods exist for spherical potentials. However, when applied to a non-spherical potential, numerical errors from the irregular solutions give rise to pathological behaviors of the charge density at small radius. Read More

Video recommendation has become an essential way of helping people explore the video world and discover the ones that may be of interest to them. However, mainstream collaborative filtering techniques usually suffer from limited performance due to the sparsity of user-video interactions, and hence are ineffective for new video recommendation. Although some recent recommender models such as CTR and CDL, have integrated text information to boost performance, user-generated videos typically include scarce or low-quality text information, which seriously degenerates performance. Read More

Learning hash functions/codes for similarity search over multi-view data is attracting increasing attention, where similar hash codes are assigned to the data objects characterizing consistently neighborhood relationship across views. Traditional methods in this category inherently suffer three limitations: 1) they commonly adopt a two-stage scheme where similarity matrix is first constructed, followed by a subsequent hash function learning; 2) these methods are commonly developed on the assumption that data samples with multiple representations are noise-free,which is not practical in real-life applications; 3) they often incur cumbersome training model caused by the neighborhood graph construction using all $N$ points in the database ($O(N)$). In this paper, we motivate the problem of jointly and efficiently training the robust hash functions over data objects with multi-feature representations which may be noise corrupted. Read More

Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Read More

In this paper, we consider the phase retrieval problem in which one aims to recover a signal from the magnitudes of affine measurements. Let $\{{\mathbf a}_j\}_{j=1}^m \subset {\mathbb H}^d$ and ${\mathbf b}=(b_1, \ldots, b_m)^\top\in{\mathbb H}^m$, where ${\mathbb H}={\mathbb R}$ or ${\mathbb C}$. We say $\{{\mathbf a}_j\}_{j=1}^m$ and $\mathbf b$ are affine phase retrievable for ${\mathbb H}^d$ if any ${\mathbf x}\in{\mathbb H}^d$ can be recovered from the magnitudes of the affine measurements $\{|<{\mathbf a}_j,{\mathbf x}>+b_j|,\, 1\leq j\leq m\}$. Read More

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. Read More

Large-scale simulations play a central role in science and the industry. Several challenges occur when building simulation software, because simulations require complex software developed in a dynamic construction process. That is why simulation software engineering (SSE) is emerging lately as a research focus. Read More

Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. Read More

The pairing mechanism of high-temperature superconductivity in cuprates remains the biggest unresolved mystery in condensed matter physics. To solve the problem, one of the most effective approaches is to investigate directly the superconducting CuO2 layers. Here, by growing CuO2 monolayer films on Bi2Sr2CaCu2O8+{\delta} substrates, we identify two distinct and spatially separated energy gaps centered at the Fermi energy, a smaller U-like gap and a larger V-like gap on the films, and study their interactions with alien atoms by low-temperature scanning tunneling microscopy. Read More

In this paper, we discuss the development of a sublinear sparse Fourier algorithm for high-dimensional data. In "Adaptive Sublinear Time Fourier Algorithm" by D. Lawlor, Y. Read More

Boson sampling, a specific quantum computation problem, is widely regarded to be one of the most achievable fields in which quantum machine will outperform the most powerful classical computer in the near term, although up to now no upper-bound of how fast the classical computers can compute matrix permanents, core problem of Boson sampling, has been reported. Here we test the computing of the matrix permanent on Tianhe-2, a supercomputer retaining its position as the world's No. 1 system for six times since June 2013. Read More

The decoy-state high-dimensional quantum key distribution provides a practical secure way to share more private information with high photon-information efficiency. In this paper, based on detector-decoy method, we propose a detector-decoy high-dimensional quantum key distribution protocol. Employing threshold detectors and a variable attenuator, we can estimate single-photon fraction of postselected events and Eves Holevo information under the Gaussian collective attack with much simpler operations in practical implementation. Read More

In this paper, we develop a framework of generalized phase retrieval in which one aims to reconstruct a vector ${\mathbf x}$ in ${\mathbb R}^d$ or ${\mathbb C}^d$ through quadratic samples ${\mathbf x}^*A_1{\mathbf x}, \dots, {\mathbf x}^*A_N{\mathbf x}$. The generalized phase retrieval includes as special cases the standard phase retrieval as well as the phase retrieval by orthogonal projections. We first explore the connections among generalized phase retrieval, low-rank matrix recovery and nonsingular bilinear form. Read More

Stochastic partition models tailor a product space into a number of rectangular regions such that the data within each region exhibit certain types of homogeneity. Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions. To alleviate this limitation, we propose a parsimonious partition model, named Stochastic Patching Process (SPP), to deal with multi-dimensional arrays. Read More

Compared with two-level quantum key distribution (QKD), highdimensional QKD enable two distant parties to share a secret key at a higher rate. We provide a finite-key security analysis for the recently proposed practical highdimensional decoy-state QKD protocol based on time-energy entanglement. We employ two methods to estimate the statistical fluctuation of the postselection probability and give a tighter bound on the secure-key capacity. Read More

To overcome the signal disturbance from the transmission process, recently, a new type of protocol named round-robin differential-phase-shift(RRDPS) quantum key distribution[Nature 509, 475(2014)] is proposed. It can estimate how much information has leaked to eavesdropper without monitoring bit error rates. In this paper, we compare the performance of RRDPS using different sources without and with decoy-state method, such as weak coherent pulses(WCPs) and heralded single photon source(HSPS). Read More

In this paper we consider the task of recognizing human actions in realistic video where human actions are dominated by irrelevant factors. We first study the benefits of removing non-action video segments, which are the ones that do not portray any human action. We then learn a non-action classifier and use it to down-weight irrelevant video segments. Read More

Merger trees are routinely used to follow the growth and merging history of dark matter haloes and subhaloes in simulations of cosmic structure formation. Srisawat et al. (2013) compared a wide range of merger-tree-building codes. Read More

The availability of low-index rutile TiO2 single crystal substrates with atomically flat surfaces is essential for enabling epitaxial growth of rutile transition metal oxide films. The high surface energy of the rutile (001) surface often leads to surface faceting, which precludes the sputter and annealing treatment commonly used for the preparation of clean and atomically flat TiO2(110) substrate surfaces. In this work, we reveal that stable and atomically flat rutile TiO2(001) surfaces can be prepared with an atomically ordered reconstructed surface already during a furnace annealing treatment in air. Read More

This paper gives a review of the recent progress in the study of Fourier bases and Fourier frames on self-affine measures. In particular, we emphasize the new matrix analysis approach for checking the completeness of a mutually orthogonal set. This method helps us settle down a long-standing conjecture that Hadamard triples generates self-affine spectral measures. Read More

One major purpose of studying the single-site scattering problem is to obtain the scattering matrices and differential equation solutions indispensable to multiple scattering theory (MST) calculations. On the other hand, the single-site scattering itself is also appealing because it reveals the physical environment experienced by electrons around the scattering center. In this paper we demonstrate a new formalism to calculate the relativistic full-potential single-site Green's function. Read More

Although the quality of quantum bits (qubits) and quantum gates has been steadily improving, the available quantity of qubits has increased quite slowly. To address this important issue in quantum computing, we have demonstrated arbitrary single qubit gates based on targeted phase shifts, an approach that can be applied to atom, ion or other atom-like systems. These gates are highly insensitive to addressing beam imperfections and have little crosstalk, allowing for a dramatic scaling up of qubit number. Read More

Substantial changes in the generation portfolio take place due to the fast growth of renewable energy generation, of which the major types such as wind and solar power have significant forecast uncertainty. Reducing the impacts of uncertainty requires the cooperation of system participants, which are supported by proper market rules and incentives. In this paper, we propose a bilateral reserve market for variable generation (VG) producers and capacity resource providers. Read More

By means of low-temperature scanning tunneling microscopy, we report on the electronic structures of BiO and SrO planes of Bi2Sr2CuO6+{\delta} (Bi-2201) superconductor prepared by argon-ion bombardment and annealing. Depending on post annealing conditions, the BiO planes exhibit either pseudogap (PG) with sharp coherence peaks and an anomalously large gap of 49 meV or van Hove singularity (VHS) near the Fermi level, while the SrO is always characteristic of a PG-like feature. This contrasts with Bi2Sr2CaCu2O8+{\delta} (Bi-2212) superconductor where VHS occurs solely on the SrO plane. Read More

We present that surface two-plasmon resonance (STPR) in electron plasma sheet produced by femtosecond laser irradiating metal surface is the self-formation mechanism of periodic subwavelength ripple structures. Peaks of overdense electrons formed by resonant two-plasmon wave pull bound ions out of the metal surface and thus the wave pattern of STPR is "carved" on the surface by Coulomb ablation (removal) resulting from the strong electrostatic field induced by charge separation. To confirm the STPR model, we have performed analogical carving experiments by two laser beams with perpendicular polarizations. Read More

The rnn package provides components for implementing a wide range of Recurrent Neural Networks. It is built withing the framework of the Torch distribution for use with the nn package. The components have evolved from 3 iterations, each adding to the flexibility and capability of the package. Read More

Twisted bilayer graphene (tBLG) forms a quasicrystal whose structural and electronic properties depend on the angle of rotation between its layers. Here we present a scanning tunneling microscopy study of gate-tunable tBLG devices supported by atomically-smooth and chemically inert hexagonal boron nitride (BN). The high quality of these tBLG devices allows identification of coexisting moir\'e patterns and moir\'e super-superlattices produced by graphene-graphene and graphene-BN interlayer interactions. Read More

Relativistic quantum mechanics predicts that when the charge of a superheavy atomic nucleus surpasses a certain threshold, the resulting strong Coulomb field causes an unusual atomic collapse state; this state exhibits an electron wave function component that falls toward the nucleus, as well as a positron component that escapes to infinity. In graphene, where charge carriers behave as massless relativistic particles, it has been predicted that highly charged impurities should exhibit resonances corresponding to these atomic collapse states. We have observed the formation of such resonances around artificial nuclei (clusters of charged calcium dimers) fabricated on gated graphene devices via atomic manipulation with a scanning tunneling microscope. Read More

We generalize the compatible tower condition given by Strichartz to the almost-Parseval-frame tower and show that non-trivial examples of almost-Parseval-frame tower exist. By doing so, we demonstrate the first singular fractal measure which has only finitely many mutually orthogonal exponentials (and hence it does not admit any exponential orthonormal bases), but it still admits Fourier frames. Read More

Understanding the mechanism of high transition temperature (Tc) superconductivity in cuprates has been hindered by the apparent complexity of their multilayered crystal structure. Using a cryogenic scanning tunneling microscopy, we report on layer-by-layer probing of the electronic structures of all ingredient planes (BiO, SrO, CuO2) of Bi2Sr2CaCu2O8+{\delta} superconductor prepared by argon-ion bombardment and annealing technique. We show that the well-known pseudogap (PG) feature observed by STM is inherently a property of the BiO planes and thus irrelevant directly to Cooper pairing. Read More

We present a proposal to realize the quantum Zeno effect (QZE) and quantum Zeno-like effect (QZLE) in a proximal $\mathrm{^{13}C}$ nuclear spin by controlling a proximal electron spin of a nitrogen vacancy (NV) center. The measurement is performed by applying a microwave pulse to induce the transition between different electronic spin states. Under the practical experimental conditions, our calculations show that there exist both QZE and QZLE in a $^{13}$C nuclear spin in the vicinity of an NV center. Read More

The reanalysis datasets provide very important sources for investigating the climate dynamics and climate changes in Antarctica. In this paper, three major reanalysis data are compared with Antarctic station data over the last 35 years: the National Centers for Environmental Prediction and the National Center for Atmospheric Research reanalysis (NCEP1), NCEP-DOE Reanalysis 2 (NCEP2), and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim). In our assessment, we compare the linear trends, the fluctuations around the trends, the persistence properties and the significance level of warming trends in the reanalysis data with the observational ones. Read More

Motivated by a recent work on the metabolism of carbohydrates in bacteria, we study the kinetics and thermodynamics of two classic models for reversible polymerization, one preserving the total polymer concentration and the other one not. The chemical kinetics is described by rate equations following the mass-action law. We consider a closed system and nonequilibrium initial conditions and show that the system dynamically evolves towards equilibrium where detailed balance is satisfied. Read More

Oceanic Kelvin and Rossby waves play an important role in tropical climate and \en dynamics. Here we develop and apply a climate network approach to quantify the characteristics of \en related oceanic waves, based on sea surface height satellite data. We associate the majority of dominant long distance ($\geq 500$ km) links of the network with (i) equatorial Kelvin waves, (ii) off-equatorial Rossby waves, and (iii) tropical instability waves. Read More

We developed a new approach for the analysis of physiological time series. An iterative convolution filter is used to decompose the time series into various components. Statistics of these components are extracted as features to characterize the mechanisms underlying the time series. Read More

O-C diagram is a useful technique to analyse the period changes of a pulsator by using the maximum (or minimum) value points which have been obtained from the historical data. But if an object is a double-mode or multi-mode pulsator, the extreme value points are the results of all the modes other than just the fundamental mode. We discussed these situations and give out some criteria to judge whether the O-C diagram can be used in these situations. Read More

We demonstrate arbitrary coherent addressing of individual neutral atoms in a $5\times 5\times 5$ array formed by an optical lattice. Addressing is accomplished using rapidly reconfigurable crossed laser beams to selectively ac Stark shift target atoms, so that only target atoms are resonant with state-changing microwaves. The effect of these targeted single qubit gates on the quantum information stored in non-targeted atoms is smaller than $3\times 10^{-3}$ in state fidelity. Read More

The aim of this paper is to study the stability of the $\ell_1$ minimization for the compressive phase retrieval and to extend the instance-optimality in compressed sensing to the real phase retrieval setting. We first show that the $m={\mathcal O}(k\log(N/k))$ measurements is enough to guarantee the $\ell_1$ minimization to recover $k$-sparse signals stably provided the measurement matrix $A$ satisfies the strong RIP property. We second investigate the phaseless instance-optimality with presenting a null space property of the measurement matrix $A$ under which there exists a decoder $\Delta$ so that the phaseless instance-optimality holds. Read More