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Hui Wang

Publications and source records attributed to Hui Wang.

At least 487 records · Page 27Linked to original sources

New class of 3D topological insulator in double perovskite

We predict a new class of three-dimensional topological insulators (TIs) in which the spin-orbit coupling (SOC) can more effectively generate a large band gap at $Γ$ point. The band gap of conventional TI such as Bi$_2$Se$_3$ is mainly limited by two factors, the strength of SOC and, from electronic structure perspective, the band gap when SOC is absent. While the former is an atomic property, we find that the latter can be minimized in a generic rock-salt lattice model in which a stable crossing of bands {\it at} the Fermi level along with band character inversion occurs for a range of parameters in the absence of SOC. Thus, large-gap TI's or TI's comprised of lighter elements can be expected. In fact, we find by performing first-principle calculations that the model applies to a class of double perovskites A$_2$BiXO$_6$ (A = Ca, Sr, Ba; X = Br, I) and the band gap is predicted up to 0.55 eV. Besides, more detailed calculations considering realistic surface structure indicate that the Dirac cones are robust against the presence of dangling bond at the boundary with a specific termination.

cond-mat.mes-hall↗

Concordance and the Smallest Covering Set of Preference Orderings

Preference orderings are orderings of a set of items according to the preferences (of judges). Such orderings arise in a variety of domains, including group decision making, consumer marketing, voting and machine learning. Measuring the mutual information and extracting the common patterns in a set of preference orderings are key to these areas. In this paper we deal with the representation of sets of preference orderings, the quantification of the degree to which judges agree on their ordering of the items (i.e. the concordance), and the efficient, meaningful description of such sets. We propose to represent the orderings in a subsequence-based feature space and present a new algorithm to calculate the size of the set of all common subsequences - the basis of a quantification of concordance, not only for pairs of orderings but also for sets of orderings. The new algorithm is fast and storage efficient with a time complexity of only $O(Nn^2)$ for the orderings of $n$ items by $N$ judges and a space complexity of only $O(\min\{Nn,n^2\})$. Also, we propose to represent the set of all $N$ orderings through a smallest set of covering preferences and present an algorithm to construct this smallest covering set. The source code for the algorithms is available at https://github.com/zhiweiuu/secs

cs.AI↗

A machine learning method for the large-scale evaluation of urban visual environment

Given the size of modern cities in the urbanising age, it is beyond the perceptual capacity of most people to develop a good knowledge about the beauty and ugliness of the city at every street corner. Correspondingly, for planners, it is also difficult to accurately answer questions like 'where are the worst-looking places in the city that regeneration should give first consideration', or 'in the fast urbanising cities, how is the city appearance changing', etc. To address this issue, we here present a computer vision method for the large-scale and automatic evaluation of the urban visual environment, by leveraging state-of-the-art machine learning techniques and the wide-coverage street view images. From the various factors that are at work, we choose two key features, the visual quality of street facade and the continuity of street wall, as the starting point of this line of analysis. In order to test the validity of this method, we further compare the machine ratings with ratings collected on site from 752 passers-by on fifty-six locations. We show that the machine learning model can produce a good estimation of people's real visual experience, and it holds much potential for various tasks in terms of urban design evaluation, culture identification, etc.

cs.CV↗

GID: Graph-based Intrusion Detection on Massive Process Traces for Enterprise Security Systems

Intrusion detection system (IDS) is an important part of enterprise security system architecture. In particular, anomaly-based IDS has been widely applied to detect abnormal process behaviors that deviate from the majority. However, such abnormal behavior usually consists of a series of low-level heterogeneous events. The gap between the low-level events and the high-level abnormal behaviors makes it hard to infer which single events are related to the real abnormal activities, especially considering that there are massive "noisy" low-level events happening in between. Hence, the existing work that focus on detecting single entities/events can hardly achieve high detection accuracy. Different from previous work, we design and implement GID, an efficient graph-based intrusion detection technique that can identify abnormal event sequences from a massive heterogeneous process traces with high accuracy. GID first builds a compact graph structure to capture the interactions between different system entities. The suspiciousness or anomaly score of process paths is then measured by leveraging random walk technique to the constructed acyclic directed graph. To eliminate the score bias from the path length, the Box-Cox power transformation based approach is introduced to normalize the anomaly scores so that the scores of paths of different lengths have the same distribution. The efficiency of suspicious path discovery is further improved by the proposed optimization scheme. We fully implement our GID algorithm and deploy it into a real enterprise security system, and it greatly helps detect the advanced threats, and optimize the incident response. Executing GID on system monitoring datasets showing that GID is efficient (about 2 million records per minute) and accurate (higher than 80% in terms of detection rate).

cs.CR↗

Tunable spin-orbit coupling and symmetry-protected edge states in graphene/WS$_2$

We demonstrate clear weak anti-localization (WAL) effect arising from induced Rashba spin-orbit coupling (SOC) in WS$_2$-covered single-layer and bilayer graphene devices. Contrary to the uncovered region of a shared single-layer graphene flake, WAL in WS$_2$-covered graphene occurs over a wide range of carrier densities on both electron and hole sides. At high carrier densities, we estimate the Rashba SOC relaxation rate to be $\sim 0.2 \rm{ps}^{-1}$ and show that it can be tuned by transverse electric fields. In addition to the Rashba SOC, we also predict the existence of a `valley-Zeeman' SOC from first-principles calculations. The interplay between these two SOC's can open a non-topological but interesting gap in graphene; in particular, zigzag boundaries host four sub-gap edge states protected by time-reversal and crystalline symmetries. The graphene/WS$_2$ system provides a possible platform for these novel edge states.

cond-mat.mes-hall↗

Information-theoretic Interestingness Measures for Cross-Ontology Data Mining

Community annotation of biological entities with concepts from multiple bio-ontologies has created large and growing repositories of ontology-based annotation data with embedded implicit relationships among orthogonal ontologies. Development of efficient data mining methods and metrics to mine and assess the quality of the mined relationships has not kept pace with the growth of annotation data. In this study, we present a data mining method that uses ontology-guided generalization to discover relationships across ontologies along with a new interestingness metric based on information theory. We apply our data mining algorithm and interestingness measures to datasets from the Gene Expression Database at the Mouse Genome Informatics as a preliminary proof of concept to mine relationships between developmental stages in the mouse anatomy ontology and Gene Ontology concepts (biological process, molecular function and cellular component). In addition, we present a comparison of our interestingness metric to four existing metrics. Ontology-based annotation datasets provide a valuable resource for discovery of relationships across ontologies. The use of efficient data mining methods and appropriate interestingness metrics enables the identification of high quality relationships.

cs.AI↗

Near Transform-Limited Single Photons from an Efficient Solid-State Quantum Emitter

By pulsed s-shell resonant excitation of a single quantum dot-micropillar system, we generate long streams of a thousand of near transform-limited single photons with high mutual indistinguishability. Hong-Ou-Mandel interference of two photons are measured as a function of their emission time separation varying from 13 ns to 14.7 μs, where the visibility slightly drops from 95.9(2)% to a plateau of 92.1(5)% through a slow dephasing process occurring at time scale of 0.7 μs. Temporal and spectral analysis reveal the pulsed resonance fluorescence single photons are close to transform limit, which are readily useful for multi-photon entanglement and interferometry experiments.

quant-ph↗

Origin and Suppression of $1/f$ Magnetic Flux Noise

Magnetic flux noise is a dominant source of dephasing and energy relaxation in superconducting qubits. The noise power spectral density varies with frequency as $1/f^α$ with $α\sim 1$ and spans 13 orders of magnitude. Recent work indicates that the noise is from unpaired magnetic defects on the surfaces of the superconducting devices. Here, we demonstrate that adsorbed molecular O$_2$ is the dominant contributor to magnetism in superconducting thin films. We show that this magnetism can be suppressed by appropriate surface treatment or improvement in the sample vacuum environment. We observe a suppression of static spin susceptibility by more than an order of magnitude and a suppression of $1/f$ magnetic flux noise power spectral density by more than a factor of 5. These advances open the door to realization of superconducting qubits with improved quantum coherence.

cond-mat.supr-con↗

Scalable boson sampling with a single-photon device

Boson sampling is a problem intractable for classical computers, but can be naturally solved on a specialized photonic quantum simulator which requires less resources than building a universal quantum computer. The biggest challenge to implement boson sampling with a large number of photons has been the lack of reliable single-photon sources. Here we demonstrate a scalable architecture of boson sampling using a solid-state single-photon source with simultaneously high efficiency, purity, and indistinguishability. The single photons are time-bin encoded and interfered in an electrically programmable loop-based network. We implement and validate boson sampling with input three and four single photons, and track the dynamical multi-photon evolution inside the circuit. With further refinement of the system efficiency, our approach may be feasible to be scaled up to >20-boson sampling to outperform classical computers, and thus provide experimental evidence against the Extended Church-Turing Thesis.

quant-ph↗

Inverse Design of Inorganic Electrides

Electrides are ionic solids that consist of cationic frameworks and anionic electrons trapped in the voids of lattices. Organic electrides exist in a large abundance, but the thermal instability at room temperature and sensitivity to moisture are bottlenecks that limit their practical uses. Known inorganic electrides are rare but appear to have high thermal and chemical stability and exhibit promising applications as electron-emitting materials, superior catalysts and strong reducing agents. Here, we report a developed inverse-design method that can be used to search for a large variety of inorganic electrides. Our method utilizes the intrinsic property of interstitial electron localization of electrides as the global variable function being incorporated into the swarm-intelligence based structure searches. Through screening 99 binary ionic compounds, we have designed 89 new inorganic electrides that are classified into three-, two-, and zero-dimensional species according to the way that the interstitial electrons are localized and the conductive properties of the systems. Our work reveals the rich abundance of inorganic electrides by extending them into more general forms and provides new structure types for electrides that are not thought of as before.

cond-mat.mtrl-sci↗

Efficient Authentication of Outsourced String Similarity Search

Cloud computing enables the outsourcing of big data analytics, where a third party server is responsible for data storage and processing. In this paper, we consider the outsourcing model that provides string similarity search as the service. In particular, given a similarity search query, the service provider returns all strings from the outsourced dataset that are similar to the query string. A major security concern of the outsourcing paradigm is to authenticate whether the service provider returns sound and complete search results. In this paper, we design AutoS3, an authentication mechanism of outsourced string similarity search. The key idea of AutoS3 is that the server returns a verification object VO to prove the result correctness. First, we design an authenticated string indexing structure named MBtree for VO construction. Second, we design two lightweight authentication methods named VS2 and EVS2 that can catch the service provider various cheating behaviors with cheap verification cost. Moreover, we generalize our solution for top k string similarity search. We perform an extensive set of experiment results on real world datasets to demonstrate the efficiency of our approach.

cs.CR↗

Devil's Staircase in an Optomechanical Cavity

We study self-excited oscillation (SEO) in an on-fiber optomechanical cavity. While the phase of SEO randomly diffuses in time when the laser power that is injected into the cavity is kept constant, phase locking may occur when the laser power is periodically modulated in time. We investigate the dependence of phase locking on the amplitude and frequency of the laser power modulation. We find that phase locking can be induced with a relatively low modulation amplitude provided that the ratio between the modulation frequency and the frequency of SEO is tuned close to a rational number of relatively low hierarchy in the Farey tree. To account for the experimental results a one dimensional map, which allows evaluating the time evolution of the phase of SEO, is theoretically derived. By calculating the winding number of the one dimensional map the regions of phase locking can be mapped in the plane of modulation amplitude and modulation frequency. Comparison between the theoretical predictions and the experimental findings yields a partial agreement.

quant-ph↗

Tunable photon blockade in a hybrid system consisting of an optomechanical device coupled to a two-level system

We study photon blockade and anti-bunching in the cavity of an optomechanical system in which the mechanical resonator is coupled to a two-level system (TLS). In particular, we analyze the effects of the coupling strength (to the mechanical mode), transition frequency, and decay rate of TLS on the photon blockade. The statistical properties of the cavity field are affected by the TLS, because the TLS changes the energy-level structure of the optomechanical system via dressed states formed by the TLS and the mechanical resonator. We find that the photon blockade and tunneling can be significantly changed by the transition frequency of the TLS and the coupling strength between the TLS and the mechanical resonator. Therefore, our study provides a method to tune the photon blockade and tunneling using a controllable TLS.

quant-ph↗

ATLAS: A Real-Space Finite-Difference Implementation of Orbital-Free Density Functional Theory

Orbital-free density functional theory (OF-DFT) is a promising method for large-scale quantum mechanics simulation as it provides a good balance of accuracy and computational cost. Its applicability to large-scale simulations has been aided by progress in constructing kinetic energy functionals and local pseudopotentials. However, the widespread adoption of OF-DFT requires further improvement in its efficiency and robustly implemented software. Here we develop a real-space finite-difference method for the numerical solution of OF-DFT in periodic systems. Instead of the traditional self-consistent method, a powerful scheme for energy minimization is introduced to solve the Euler--Lagrange equation. Our approach engages both the real-space finite-difference method and a direct energy-minimization scheme for the OF-DFT calculations. The method is coded into the ATLAS software package and benchmarked using periodic systems of solid Mg, Al, and Al$_{3}$Mg. The test results show that our implementation can achieve high accuracy, efficiency, and numerical stability for large-scale simulations.

physics.comp-ph↗

Managing Hybrid Main Memories with a Page-Utility Driven Performance Model

Hybrid memory systems comprised of dynamic random access memory (DRAM) and non-volatile memory (NVM) have been proposed to exploit both the capacity advantage of NVM and the latency and dynamic energy advantages of DRAM. An important problem for such systems is how to place data between DRAM and NVM to improve system performance. In this paper, we devise the first mechanism, called UBM (page Utility Based hybrid Memory management), that systematically estimates the system performance benefit of placing a page in DRAM versus NVM and uses this estimate to guide data placement. UBM's estimation method consists of two major components. First, it estimates how much an application's stall time can be reduced if the accessed page is placed in DRAM. To do this, UBM comprehensively considers access frequency, row buffer locality, and memory level parallelism (MLP) to estimate the application's stall time reduction. Second, UBM estimates how much each application's stall time reduction contributes to overall system performance. Based on this estimation method, UBM can determine and place the most critical data in DRAM to directly optimize system performance. Experimental results show that UBM improves system performance by 14% on average (and up to 39%) compared to the best of three state-of-the-art mechanisms for a large number of data-intensive workloads from the SPEC CPU2006 and Yahoo Cloud Serving Benchmark (YCSB) suites.

cs.AR↗

OR-Benchmark: An Open and Reconfigurable Digital Watermarking Benchmarking Framework

Benchmarking digital watermarking algorithms is not an easy task because different applications of digital watermarking often have very different sets of requirements and trade-offs between conflicting requirements. While there have been some general-purpose digital watermarking benchmarking systems available, they normally do not support complicated benchmarking tasks and cannot be easily reconfigured to work with different watermarking algorithms and testing conditions. In this paper, we propose OR-Benchmark, an open and highly reconfigurable general-purpose digital watermarking benchmarking framework, which has the following two key features: 1) all the interfaces are public and general enough to support all watermarking applications and benchmarking tasks we can think of; 2) end users can easily extend the functionalities and freely configure what watermarking algorithms are tested, what system components are used, how the benchmarking process runs, and what results should be produced. We implemented a prototype of this framework as a MATLAB software package and used it to benchmark a number of digital watermarking algorithms involving two types of watermarks for content authentication and self-restoration purposes. The benchmarking results demonstrated the advantages of the proposed benchmarking framework, and also gave us some useful insights about existing image authentication and self-restoration watermarking algorithms which are an important but less studied topic in digital watermarking.

cs.MM↗

Wireless Multicast for Zoomable Video Streaming

Zoomable video streaming refers to a new class of interactive video applications, where users can zoom into a video stream to view a selected region of interest in higher resolutions and pan around to move the region of interest. The zoom and pan effects are typically achieved by breaking the source video into a grid of independently decodable tiles. Streaming the tiles to a set of heterogeneous users using broadcast is challenging, as users have different link rates and different regions of interest at different resolution levels. In this paper, we consider the following problem: given the subset of tiles that each user requested, the link rate of each user, and the available time slots, at which resolution should each tile be sent, to maximize the overall video quality received by all users. We design an efficient algorithm to solve the problem above, and evaluate the solution on a testbed using 10 mobile devices. Our method is able to achieve up to 12dB improvements over other heuristic methods.

cs.NI↗

Candidate source of flux noise in SQUIDs: adsorbed oxygen molecules

A major obstacle to using SQUIDs as qubits is flux noise. We propose that the heretofore mysterious spins producing flux noise could be $O_2$ molecules adsorbed on the surface. Using density functional theory calculations, we find that an $O_2$ molecule adsorbed on an α-alumina surface has a magnetic moment of ~1.8 μB. When the spin is oriented perpendicular to the axis of the O-O bond, the barrier to spin rotations is about 10 mK. Monte Carlo simulations of ferromagnetically coupled, anisotropic XY spins on a square lattice find 1/f magnetization noise, consistent with flux noise in Al SQUIDs.

cond-mat.supr-con↗