arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 1,279 records · Page 71Linked to original sources

arXiv2018

Efficiency and power of minimally nonlinear irreversible heat engines with broken time-reversal symmetry

We study the minimally nonlinear irreversible heat engines in which the time-reversal symmetry for the systems may b e broken. The expressions for the power and the efficiency are derived, in which the effects of the nonlinear terms due to dissipations are included. We show that, as within the linear responses, the minimally nonlinear irreversible heat engines enable attainment of Carnot efficiency at positive power. We also find that the Curzon-Ahlborn limit imposed on the efficiency at maximum power can be overcomed if the time-reversal symmetry is broken.

cond-mat.stat-mech↗

arXiv2018

Observation of tunneling gap in epitaxial ultrathin films of pyrite-type copper disulfide

We report scanning tunneling microscopy investigation on epitaxial ultrathin films of pyrite-type copper disulfide. Layer by layer growth of CuS2 films with a preferential orientation of (111) on SrTiO3(001) and Bi2Sr2CaCu2O8+δ substrates is achieved by molecular beam epitaxy growth. For ultrathin films on both kinds of substrates, we observed symmetric tunneling gap around Fermi level that persists up to ~ 15 K. The tunneling gap degrades with either increasing temperature or increasing thickness, suggesting new matter states at the extreme two dimensional limit.

cond-mat.supr-con↗

arXiv2018

Improving Word Vector with Prior Knowledge in Semantic Dictionary

Using low dimensional vector space to represent words has been very effective in many NLP tasks. However, it doesn't work well when faced with the problem of rare and unseen words. In this paper, we propose to leverage the knowledge in semantic dictionary in combination with some morphological information to build an enhanced vector space. We get an improvement of 2.3% over the state-of-the-art Heidel Time system in temporal expression recognition, and obtain a large gain in other name entity recognition (NER) tasks. The semantic dictionary Hownet alone also shows promising results in computing lexical similarity.

cs.CL↗

arXiv2018

Tensor network and ($p$-adic) AdS/CFT

We use the tensor network living on the Bruhat-Tits tree to give a concrete realization of the recently proposed $p$-adic AdS/CFT correspondence (a holographic duality based on the $p$-adic number field $\mathbb{Q}_p$). Instead of assuming the $p$-adic AdS/CFT correspondence, we show how important features of AdS/CFT such as the bulk operator reconstruction and the holographic computation of boundary correlators are automatically implemented in this tensor network.

hep-th↗

arXiv2018

Toward Scalable Boson Sampling with Photon Loss

Boson sampling is a well-defined task that is strongly believed to be intractable for classical computers, but can be efficiently solved by a specific quantum simulator. However, an outstanding problem for large-scale experimental boson sampling is the scalability. Here we report an experiment on boson sampling with photon loss, and demonstrate that boson sampling with a few photons lost can increase the sampling rate. Our experiment uses a quantum-dot-micropillar single-photon source demultiplexed into up to seven input ports of a 16*16 mode ultra-low-loss photonic circuit, and we detect three-, four- and five-fold coincidence counts. We implement and validate lossy boson sampling with one and two photons lost, and obtain sampling rates of 187 kHz, 13.6 kHz, and 0.78 kHz for five-, six- and seven-photon boson sampling with two photons lost, which is 9.4, 13.9, and 18.0 times faster than the standard boson sampling, respectively. Our experiment shows an approach to significantly enhance the sampling rate of multiphoton boson sampling.

quant-ph↗

arXiv2018

18-qubit entanglement with photon's three degrees of freedom

A central theme in quantum information science is to coherently control an increasing number of quantum particles as well as their internal and external degrees of freedom (DoFs), meanwhile maintaining a high level of coherence. The ability to create and verify multiparticle entanglement with individual control and measurement of each qubit serves as an important benchmark for quantum technologies. To this end, genuine multipartite entanglement have been reported up to 14 trapped ions, 10 photons, and 10 superconducting qubits. Here, we experimentally demonstrate an 18-qubit Greenberger-Horne-Zeilinger (GHZ) entanglement by simultaneous exploiting three different DoFs of six photons, including their paths, polarization, and orbital angular momentum (OAM). We develop high-stability interferometers for reversible quantum logic operations between the photon's different DoFs with precision and efficiencies close to unity, enabling simultaneous readout of 262,144 outcome combinations of the 18-qubit state. A state fidelity of 0.708(16) is measured, confirming the genuine entanglement of all the 18 qubits.

quant-ph↗

arXiv2018

Time-Stretch Probing of Ultrafast Soliton Molecule Dynamics

Soliton molecules are the manifestation of attractive and repulsive interaction between optical pulses mediated by a nonlinear medium. However, the formation and breakup of soliton molecules are difficult to observe due to the transient nature of the process. Using the time stretch technique, we have been able to track the real-time evolution of the bound state formation in a femtosecond fiber laser and unveil different evolution paths towards a stable bound state. A non-stationary Q-switched mode-locking regime consisting of various transient solitons is observed in this transition period. We also observe additional dynamics including soliton molecule vibrations, collision and decay. Our findings uncover a diverse set of soliton dynamics in an optical nonlinear system and provide valuable data for further theoretical studies.

physics.optics↗

arXiv2018

BigRoots: An Effective Approach for Root-cause Analysis of Stragglers in Big Data System

Stragglers are commonly believed to have a great impact on the performance of big data system. However, the reason to cause straggler is complicated. Previous works mostly focus on straggler detection, schedule level optimization and coarse-grained cause analysis. These methods cannot provide valuable insights to help users optimize their programs. In this paper, we propose BigRoots, a general method incorporating both framework and system features for root-cause analysis of stragglers in big data system. BigRoots considers features from big data framework such as shuffle read/write bytes and JVM garbage collection time, as well as system resource utilization such as CPU, I/O and network, which is able to detect both internal and external root causes of stragglers. We verify BigRoots by injecting high resource utilization across different system components and perform case studies to analyze different workloads in Hibench. The experimental results demonstrate that BigRoots is effective to identify the root cause of stragglers and provide useful guidance for performance optimization.

cs.DC↗

arXiv2017

An extensive impurity-scattering study on the pairing symmetry of monolayer FeSe films on SrTiO3

Determination of the pairing symmetry in monolayer FeSe films on SrTiO3 is a requisite for understanding the high superconducting transition temperature in this system, which has attracted intense theoretical and experimental studies but remains controversial. Here, by introducing several types of point defects in FeSe monolayer films, we conduct a systematic investigation on the impurity-induced electronic states by spatially resolved scanning tunneling spectroscopy. Ranging from surface adsorption, chemical substitution to intrinsic structural modification, these defects generate a variety of scattering strength, which renders new insights on the pairing symmetry.

cond-mat.supr-con↗

arXiv2017

Closed-Form Error Analysis on RSS-based Indoor Localization Method

Received Signal Strength (RSS) is considered as a promising measurement for indoor positioning. Lots of RSS-based localization methods have been proposed by its convenience and low cost. This paper focuses on two challenging issues in RSS-based localization schemes: finding optimal localization algorithms and knowing what affects the accuracy of these algorithms. Through theoretical and experimental analysis, we present three important results: 1) we prove that the Non-linear Least Square (NLS) method is efficient to solve the RSS-based localization problem, i.e., its localization error is minimal in theory; 2) we provide a closed-form expression of the localization error for the NLS method. Such an expression reveals the key factors that affect the accuracy of the NLS method; 3) we further conduct a new lower bound of localization error, i.e., the best accuracy achieved possibly. The study of this paper shows the inherent limitation of RSS-based localization schemes and provides guidance for achieving optimal accuracy.

cs.NI↗

arXiv2017

Stock market as temporal network

Financial networks have become extremely useful in characterizing the structure of complex financial systems. Meanwhile, the time evolution property of the stock markets can be described by temporal networks. We utilize the temporal network framework to characterize the time-evolving correlation-based networks of stock markets. The market instability can be detected by the evolution of the topology structure of the financial networks. We employ the temporal centrality as a portfolio selection tool. Those portfolios, which are composed of peripheral stocks with low temporal centrality scores, have consistently better performance under different portfolio optimization schemes, suggesting that the temporal centrality measure can be used as new portfolio optimization and risk management tools. Our results reveal the importance of the temporal attributes of the stock markets, which should be taken serious consideration in real life applications.

q-fin.ST↗

arXiv2017

The effect of the fluctuating proton size on the study of chiral magnetic effect in proton-nucleus collisions

High energy proton-nucleus (pA) collisions provide an important constraint on the study of the chiral magnetic effect in QCD matter. Naively, in pA collisions one expects no correlation between the orientation of event plane as reconstructed from the azimuthal distribution of produced hadrons and the orientation of magnetic field. If this is the case, any charge-dependent hadron correlations can only result from the background. Nevertheless, in this paper we point out that in high multiplicity pA collisions a correlation between the magnetic field and the event plane can appear. This is because triggering on the high hadron multiplicity amounts to selecting Fock components of the incident proton with a large number of partons that are expected to have a transverse size much larger than the average proton size. We introduce the effect of the fluctuating proton size in the Monte Carlo Glauber model and evaluate the resulting correlation between the magnetic field and the second-order event plane in both pA and nucleus-nucleus (AA) collisions. The fluctuating proton size is found to result in a significant correlation between magnetic field and the event plane in pA collisions, even though the magnitude of the correlation is still much smaller than in AA collisions. This result opens a possibility of studying the chiral magnetic effect in small systems.

nucl-th↗

arXiv2017

Two-dimensional material based "field-effect CCD"

Charge-coupled device (CCD), along with the complementary metal-oxide-semiconductor (CMOS), is one of the major imaging technologies. Traditional CCDs rely on the charge transfer between potential wells, which gives them advantages of simple pixel structure, high sensitivity, and low noise. However, the serial transfer requires fabrication incompatible with CMOS, and leads to slower, more complex, and less flexible readout than random access. Here, we report a new-concept CCD called field-effect CCD (FE-CCD), which is based on the capacitive "coupling" between the semiconductor substrate and the 2D material. The strong field effect of the 2D material enables amplification, and non-destructive readout of the integrated charges in one pixel without transfer. We demonstrated the broadband response of our CCD (up to 1870 nm) and the successful integration of 2D hetero-structures for reducing the power consumption. The demonstrated FE-CCD provides a valuable strategy for versatile 2D materials to be monolithically integrated into the semiconductor imaging technology.

physics.app-ph↗

arXiv2017

Critical behavior of a stochastic anisotropic Bak-Sneppen model

In this paper we present our study on the critical behavior of a stochastic anisotropic Bak-Sneppen (saBS) model, in which a parameter $α$ is introduced to describe the interaction strength among nearest species. We estimate the threshold fitness $f_c$ and the critical exponent $τ_r$ by numerically integrating a master equation for the distribution of avalanche spatial sizes. Other critical exponents are then evaluated from previously known scaling relations. The numerical results are in good agreement with the counterparts yielded by the Monte Carlo simulations. Our results indicate that all saBS models with nonzero interaction strength exhibit self-organized criticality, and fall into the same universality class, by sharing the universal critical exponents.

nlin.AO↗

arXiv2017

Community Detection in Dynamic Networks via Adaptive Label Propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.

physics.soc-ph↗

arXiv2017

Surface Plasmon Enhanced Strong Exciton-Photon Coupling in Hybrid Inorganic-Organic Perovskites Nanowires

Manipulating strong light-matter interaction in semiconductor microcavities is crucial for developing high-performance exciton polariton devices with great potentials in next-generation all-solid state quantum technologies. In this work, we report surface plasmon enhanced strong exciton-photon interaction in CH3NH3PbBr3 perovskite nanowires. Characteristic anti-crossing behaviors, indicating Rabi splitting energy up to ~ 560 meV, are observed near exciton resonance in hybrid semiconductor-insulator-metallic waveguide cavity at room temperature. An exciton-photon coupling strength enhancement factor of ~ 1.4 times is evaluated, which is mainly attributed to surface plasmon induced localized excitation field redistribution. Further, systematic studies on nanowires and insulator dimension dependence of exciton-photon interaction are presented. These results provide new avenues to achieve extremely high coupling strengths and push forward the development of electrically pumped and ultra-low threshold small lasers.

cond-mat.mes-hall↗

arXiv2017

Deep Residual Text Detection Network for Scene Text

Scene text detection is a challenging problem in computer vision. In this paper, we propose a novel text detection network based on prevalent object detection frameworks. In order to obtain stronger semantic feature, we adopt ResNet as feature extraction layers and exploit multi-level feature by combining hierarchical convolutional networks. A vertical proposal mechanism is utilized to avoid proposal classification, while regression layer remains working to improve localization accuracy. Our approach evaluated on ICDAR2013 dataset achieves F-measure of 0.91, which outperforms previous state-of-the-art results in scene text detection.

cs.CV↗

arXiv2017

Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models

Traditional Chinese Medicine (TCM) has accumulated a big amount of precious resource in the long history of development. TCM prescriptions that consist of TCM herbs are an important form of TCM treatment, which are similar to natural language documents, but in a weakly ordered fashion. Directly adapting language modeling style methods to learn the embeddings of the herbs can be problematic as the herbs are not strictly in order, the herbs in the front of the prescription can be connected to the very last ones. In this paper, we propose to represent TCM herbs with distributed representations via Prescription Level Language Modeling (PLLM). In one of our experiments, the correlation between our calculated similarity between medicines and the judgment of professionals achieves a Spearman score of 55.35 indicating a strong correlation, which surpasses human beginners (TCM related field bachelor student) by a big margin (over 10%).

cs.CL↗