arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 1,153 records · Page 64Linked to original sources

arXiv2019

Strong Pauli paramagnetic effect in the upper critical field of KCa$_2$Fe$_4$As$_4$F$_2$

Recently, 12442 system of Fe-based superconductors has attracted considerable attention owing to its unique double-FeAs-layer structure. A steep increase in the in-plane upper critical field with cooling has been observed near the superconducting transition temperature, $T_c$, in KCa$_2$Fe$_4$As$_4$F$_2$ single crystals. Herein, we report a high-field investigation on upper critical field of this material over a wide temperature range, and both out-of-plane ($H\|c$, $H_{c2}^{c}$) and in-plane ($H\|ab$, $H_{c2}^{ab}$) directions have been measured. A sublinear temperature-dependent behavior is observed for the out-of-plane $H_{c2}^{c}$, whereas strong convex curvature with cooling is observed for the in-plane $H_{c2}^{ab}$. Such behaviors could not be described by the conventional Werthamer--Helfand--Hohenberg (WHH) model. The data analysis based on the WHH model by considering the spin aspects reveals a large Maki parameter $α=9$, indicating that the in-plane upper critical field is affected by a very strong Pauli paramagnetic effect.

cond-mat.supr-con↗

arXiv2019

Upper Bound of Collective Attacks on Quantum Key Distribution

Evaluating the theoretical limit of the amount of information Eve can steal from a quantum key distribution protocol under given conditions is one of the most important things that need to be done in security proof. In addition to source loopholes and detection loopholes, channel attacks are considered to be the main ways of information leakage, while collective attacks are considered to be the most powerful active channel attacks. Here we deduce in detail the capability limit of Eve's collective attack in non-entangled quantum key distribution, like BB84 and measurement-device-independent protocols, and entangled quantum key distribution, like device-independent protocol, in which collective attack is composed of quantum weak measurement and quantum unambiguous state discrimination detection. The theoretical results show that collective attacks are equivalent in entangled and non-entangled quantum key distribution protocols. We also find that compared with the security proof based on entanglement purification, the security proof based on collective attack not only improves the system's tolerable bit error rate, but also improves the key rate.

cs.CR↗

arXiv2019

Insulator-metal transition and quasi-flat-band of Shastry-Sutherland lattice

Insulator-metal transition is investigated self-consistently on the frustrated Shastry-Sutherland lattice in the framework of Slave-Boson mean-field theory. Due to the presence of quasi-flat band structure characteristic, the system displays a spin-density-wave (SDW) insulating phase at the weak doping levels, which is robust against frustration, and it will be transited into an SDW metallic phase at high doping levels. As further increasing the doping, the temperature or the frustration on the diagonal linking bonds, the magnetic order $m$ will be monotonically suppressed, resulting in the appearance of a paramagnetic metallic phase. Although the Fermi surface of the SDW metallic phase may be immersed by temperature, the number of mobile charges is robust against temperature at weak doping levels.

cond-mat.str-el↗

arXiv2019

Anomalous sharp peak in the London penetration depth induced by the nodeless-to-nodal superconducting transition in BaFe$_2$(As$_{1-x}$P$_x$)$_2$

The issue of whether the quantum critical point (QCP) is hidden inside unconventional superconductors is a matter of hot debate. Although a prominent experiment on London penetration depth has demonstrated the existence of the QCP in the isovalent-doped iron-based superconductor BaFe$_2$(As$_{1-x}$P$_x$)$_2$, with the observation of a sharp peak in the penetration depth in the vicinity of the disappearance of magnetic order at zero temperature, the nature of such an emerging QCP remains unclear. Here, we provide a unique picture to understand well the phenomena of the QCP based on the framework of linear response theory. Evidence from the density of states and superfluid density calculations suggests the nodeless-to-nodal pairing transition accompanied the appearance of a sharp peak in the penetration depth in BaFe$_2$(As$_{1-x}$P$_x$)$_2$. Such a pairing transition originates from the three-dimensional electronic properties with a strong interlayer superconducting pairing. This finding provides a significant insight into the understanding of the QCP observed in experiment in BaFe$_2$(As$_{1-x}$P$_x$)$_2$.

cond-mat.supr-con↗

arXiv2019

Multi-level Gated Recurrent Neural Network for Dialog Act Classification

In this paper we focus on the problem of dialog act (DA) labelling. This problem has recently attracted a lot of attention as it is an important sub-part of an automatic question answering system, which is currently in great demand. Traditional methods tend to see this problem as a sequence labelling task and deals with it by applying classifiers with rich features. Most of the current neural network models still omit the sequential information in the conversation. Henceforth, we apply a novel multi-level gated recurrent neural network (GRNN) with non-textual information to predict the DA tag. Our model not only utilizes textual information, but also makes use of non-textual and contextual information. In comparison, our model has shown significant improvement over previous works on Switchboard Dialog Act (SWDA) task by over 6%.

cs.CL↗

arXiv2019

Phase Matching Quantum Key Distribution based on Single-Photon Entanglement

Two time-reversal quantum key distribution (QKD) schemes are the quantum entanglement based device-independent (DI)-QKD and measurement-device-independent (MDI)-QKD. The recently proposed twin field (TF)-QKD, also known as phase-matching (PM)-QKD, has improved the key rate bound from $O\left( η\right )$ to $O\left( \sqrt η \right )$ with $η$ the channel transmittance. In fact, TF-QKD is a kind of MDI-QKD but based on single-photon detection. In this paper, we propose a different PM-QKD based on single-photon entanglement, referred to as single-photon entanglement-based phase-matching (SEPM)-QKD, which can be viewed as a time-reversed version of the TF-QKD. Detection loopholes of the standard Bell test, which often occur in DI-QKD over long transmission distances, are not present in this protocol because the measurement settings and key information are the same quantity which is encoded in the local weak coherent state. We give a security proof of SEPM-QKD and demonstrate in theory that it is secure against all collective attacks and beam-splitting attacks. The simulation results show that the key rate enjoys a bound of $O\left( \sqrt η \right )$ with respect to the transmittance. SEPM-QKD not only helps us understand TF-QKD more deeply, but also hints at a feasible approach to eliminate detection loopholes in DI-QKD for long-distance communications.

quant-ph↗

arXiv2019

Conductivities of magnetic quark-gluon plasma at strong coupling

In the presence of a strong magnetic field, the quark gluon plasma is magnetized, leading to anisotropic transport coefficients. In this work, we focus on the effect of magnetization on electric conductivity, ignoring the possible contribution from the axial anomaly. We generalize longitudinal and transverse conductivities to finite frequencies. For transverse conductivity, a separation of contribution from fluid velocity is needed. We study the dependence of the conductivities on the magnetic field and frequency using a holographic magnetic brane model. The longitudinal conductivity scales roughly linearly in the magnetic field, while the transverse conductivity is rather insensitive to the magnetic field. Furthermore, we find the conductivities can be significantly enhanced at large frequency. This can possibly extend the lifetime of the magnetic field, which is a key component of the chiral magnetic effect.

hep-th↗

arXiv2019

Inverse transport problem in fluorescence ultrasound modulated optical tomography with angularly averaged measurements

We consider an inverse transport problem in fluorescence ultrasound modulated optical tomography (fUMOT) with angularly averaged illuminations and measurements. We study the uniqueness and stability of the reconstruction of the absorption coefficient and the quantum efficiency of the fluorescent probes. Reconstruction algorithms are proposed and numerical validations are performed as well. This paper is an extension of arXiv:1804.01135, where a diffusion model for this problem was considered.

math-ph↗

arXiv2019

Recursive Graphical Neural Networks for Text Classification

The complicated syntax structure of natural language is hard to be explicitly modeled by sequence-based models. Graph is a natural structure to describe the complicated relation between tokens. The recent advance in Graph Neural Networks (GNN) provides a powerful tool to model graph structure data, but simple graph models such as Graph Convolutional Networks (GCN) suffer from over-smoothing problem, that is, when stacking multiple layers, all nodes will converge to the same value. In this paper, we propose a novel Recursive Graphical Neural Networks model (ReGNN) to represent text organized in the form of graph. In our proposed model, LSTM is used to dynamically decide which part of the aggregated neighbor information should be transmitted to upper layers thus alleviating the over-smoothing problem. Furthermore, to encourage the exchange between the local and global information, a global graph-level node is designed. We conduct experiments on both single and multiple label text classification tasks. Experiment results show that our ReGNN model surpasses the strong baselines significantly in most of the datasets and greatly alleviates the over-smoothing problem.

cs.CL↗

arXiv2019

OpenMPL: An Open Source Layout Decomposer

Multiple patterning lithography has been widely adopted in advanced technology nodes of VLSI manufacturing. As a key step in the design flow, multiple patterning layout decomposition (MPLD) is critical to design closure. Due to the NP-hardness of the general decomposition problem, various efficient algorithms have been proposed with high quality solutions. However, with increasingly complicated design flow and peripheral processing steps, developing a high-quality layout decomposer becomes more and more difficult, slowing down the further advancement in this field. This paper presents OpenMPL [1], an open-source layout decomposition framework, with well-separated peripheral processing and the core solving steps. We demonstrate the flexibility of the framework with efficient implementations of various state-of-the-art algorithms, which enable us to reproduce most of the recent results on widely-recognized benchmarks. We believe OpenMPL can pave the road for developing layout decomposition engines and stimulate further researches on this problem.

cs.OH↗

arXiv2019

Two-Pass End-to-End Speech Recognition

The requirements for many applications of state-of-the-art speech recognition systems include not only low word error rate (WER) but also low latency. Specifically, for many use-cases, the system must be able to decode utterances in a streaming fashion and faster than real-time. Recently, a streaming recurrent neural network transducer (RNN-T) end-to-end (E2E) model has shown to be a good candidate for on-device speech recognition, with improved WER and latency metrics compared to conventional on-device models [1]. However, this model still lags behind a large state-of-the-art conventional model in quality [2]. On the other hand, a non-streaming E2E Listen, Attend and Spell (LAS) model has shown comparable quality to large conventional models [3]. This work aims to bring the quality of an E2E streaming model closer to that of a conventional system by incorporating a LAS network as a second-pass component, while still abiding by latency constraints. Our proposed two-pass model achieves a 17%-22% relative reduction in WER compared to RNN-T alone and increases latency by a small fraction over RNN-T.

cs.CL↗

arXiv2019

A Center-Point Algorithm for Unit Commitment with Carbon Emission Trading

This paper proposes a global optimization method for it ensures finding good solutions while solving the unit commitment (UC) problem with carbon emission trading (CET). This method con-sists of two parts. In the first part, a sequence of linear inte-ger-relaxed subproblems are first solved to rapidly generate a tight linear relaxation of the original mixed integer nonlinear pro-gramming problem (MINLP) model. In the second part, the algo-rithm introduces the idea of center-cut so that it can quickly find good solutions. The approach tested on 10 test instances with units ranging from 35 to 1560 over a scheduling period of 24h, and compared with state-of-the-art solver CPLEX. The results show that the proposed algorithm can find better solutions than CPLEX in a short time. And it is more suitable to solve large scale UC problem than CPLEX.

math.OC↗

arXiv2019

A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference. Both low-rankness and sparsity are appealing properties for the network approximation. In this paper we propose a unified framework to compress the convolutional neural networks (CNNs) by combining these two properties, while taking the nonlinear activation into consideration. Each layer in the network is approximated by the sum of a structured sparse component and a low-rank component, which is formulated as an optimization problem. Then, an extended version of alternating direction method of multipliers (ADMM) with guaranteed convergence is presented to solve the relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet and GoogLeNet with large image classification datasets. The results outperform previous work in terms of accuracy degradation, compression rate and speedup ratio. The proposed method is able to remarkably compress the model (with up to 4.9x reduction of parameters) at a cost of little loss or without loss on accuracy.

cs.LG↗

arXiv2019

Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection

Learning to localize and name object instances is a fundamental problem in vision, but state-of-the-art approaches rely on expensive bounding box supervision. While weakly supervised detection (WSOD) methods relax the need for boxes to that of image-level annotations, even cheaper supervision is naturally available in the form of unstructured textual descriptions that users may freely provide when uploading image content. However, straightforward approaches to using such data for WSOD wastefully discard captions that do not exactly match object names. Instead, we show how to squeeze the most information out of these captions by training a text-only classifier that generalizes beyond dataset boundaries. Our discovery provides an opportunity for learning detection models from noisy but more abundant and freely-available caption data. We also validate our model on three classic object detection benchmarks and achieve state-of-the-art WSOD performance. Our code is available at https://github.com/yekeren/Cap2Det.

cs.CV↗

arXiv2019

Universal critical behavior in the ferromagnetic superconductor Eu(Fe$_{0.75}$Ru$_{0.25}$)$_{2}$As$_{2}$

The study of universal critical behavior is a crucial issue in a continuous phase transition, which groups various critical phenomena into universality classes for revealing microscopic electronic behaviors. The understanding of the nature of magnetism in Eu-based ferromagnetic superconductors is largely impeded by the infeasibility of performing inelastic neutron scattering measurements to deduce the microscopic magnetic behaviors and the effects on the superconductivity, due to the significant neutron absorption effect of natural $^{152}$Eu and unavailability of large single crystals. However, by systematically combining the neutron diffraction experiment, the first-principles calculations, and the quantum Monte Carlo simulations, we have obtained a perfectly consistent universal critical exponent value of $β=0.385(13)$ experimentally and theoretically for Eu(Fe$_{0.75}$Ru$_{0.25}$)$_{2}$As$_{2}$, from which the magnetism in the Eu-based ferromagnetic superconductors is identified as the universal class of a three-dimensional anisotropic quantum Heisenberg model with long-range magnetic exchange coupling. This study not only clarifies the nature of microscopic magnetic behaviors in the Eu-based ferromagnetic superconductors, but also opens a new avenue of systemic methodology for studying the universal critical behaviors associated with magnetic phase transitions in the area of magnetism and the spin fluctuations effects on the unconventional superconductivity.

cond-mat.str-el↗

arXiv2019

On rank estimators in increasing dimensions

The family of rank estimators, including Han's maximum rank correlation (Han, 1987) as a notable example, has been widely exploited in studying regression problems. For these estimators, although the linear index is introduced for alleviating the impact of dimensionality, the effect of large dimension on inference is rarely studied. This paper fills this gap via studying the statistical properties of a larger family of M-estimators, whose objective functions are formulated as U-processes and may be discontinuous in increasing dimension set-up where the number of parameters, $p_{n}$, in the model is allowed to increase with the sample size, $n$. First, we find that often in estimation, as $p_{n}/n\rightarrow 0$, $(p_{n}/n)^{1/2}$ rate of convergence is obtainable. Second, we establish Bahadur-type bounds and study the validity of normal approximation, which we find often requires a much stronger scaling requirement than $p_{n}^{2}/n\rightarrow 0.$ Third, we state conditions under which the numerical derivative estimator of asymptotic covariance matrix is consistent, and show that the step size in implementing the covariance estimator has to be adjusted with respect to $p_{n}$. All theoretical results are further backed up by simulation studies.

math.ST↗

arXiv2019

Wave-Particle duality in Single-Photon Entanglement

The simplest single-photon entanglement is the entanglement of the vacuum state and the single-photon state between two path modes. The verification of the existence of single-photon entanglement has attracted extensive research interests. Here, based on the construction of Bell's inequality in wave space, we propose a new method to verify single photon entanglement. Meanwhile, we define the wave state in two-dimensional space relative to the photon number state, and propose a method to measure it. Strong violation of Bell inequality based on joint measurement of wave states indicates the existence of single photon entanglement with certainty. Wave state entanglement obtained from Fourier transform of single photon entanglement and the corresponding measurement protocols will provide us with more information-carrying schemes in the field of quantum information. The difference in the representation in photon-number space and wave space implies the wave-particle duality of single photon entanglement.

quant-ph↗

arXiv2019

MIN: Co-Governing Multi-Identifier Network Architecture and its Prototype on Operator's Network

IP protocol is the core of TCP/IP network layer. However, since IP address and its Domain Name are allocated and managed by a single agency, there are risks of centralization. The semantic overload of IP address also reduces its scalability and mobility, which further hinders the security. This paper proposes a co-governing Multi-Identifier Network (MIN) architecture that constructs a network layer with parallel coexistence of multiple identifiers, including identity, content, geographic information, and IP address. On the management plane, we develop an efficient management system using consortium blockchain with voting consensus, so the network can simultaneously manage and support by hundreds or thousands of nodes with high throughput. On the data plane, we propose an algorithm merging hash table and prefix tree (HTP) for FIB, which avoids the false-negative error and can inter-translate different identifiers with tens of billions of entries. Further, we propose a scheme to transport IP packets using CCN as a tunnel for supporting progressive deployment. We deployed the prototype of MIN to the largest operators' network in Mainland China, Hongkong and Macao, and demonstrated that the network can register identifier under co-governing consensus algorithm, support VoD service very well.

cs.NI↗