arXiv subjects

Wei Li

Publications and source records attributed to Wei Li.

At least 1,189 records · Page 66Linked to original sources

arXiv2019

Thermal Diffusion and Quantum Chaos in Neutral Magnetized Plasma

We calculate the thermal diffusion constant $D_T$ and butterfly velocity $v_B$ in neutral magnetized plasma using holographic magnetic brane background. We find the thermal diffusion constant satisfies Blake's bound. The constant in the bound $D_T2πT/v_B^2$ is a decreasing function of magnetic field. It approaches one half in the large magnetic field limit. We also find the existence of a special point defined by Lyapunov exponent and butterfly velocity on which pole-skipping phenomenon occurs.

hep-th↗

arXiv2019

Behavior Sequence Transformer for E-commerce Recommendation in Alibaba

Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendations. However, most of these works just concatenate different features, ignoring the sequential nature of users' behaviors. In this paper, we propose to use the powerful Transformer model to capture the sequential signals underlying users' behavior sequences for recommendation in Alibaba. Experimental results demonstrate the superiority of the proposed model, which is then deployed online at Taobao and obtain significant improvements in online Click-Through-Rate (CTR) comparing to two baselines.

cs.IR↗

arXiv2019

Beta-PtO2: Phononic, thermodynamic, and elastic properties derived from first-principles calculations

Beta-PtO2 is a useful transition metal dioxide, but its fundamental thermodynamic and elastic properties remain unexplored. Using first-principles calculations, we systematically studied the structure, phonon, thermodynamic and elastic properties of beta-PtO2. The lattice dynamics and structural stability of beta-PtO2 under pressure were studied using the phonon spectra and vibrational density of states. The vibrational frequencies of the optical modes of beta-PtO2 increase with elevating pressure; this result is comparable with the available experimental data. Then, the heat capacities and their pressure responses were determined based on the phonon calculations. The pressure dependence of the Debye temperature was studied, and the results are compared in two distinct aspects. The elastic moduli of beta-PtO2 were estimated through the Voigt-Reuss-Hill approximation. The bulk modulus of beta-PtO2 increases linearly with pressure, but the shear modulus is nearly independent of pressure. Our study revealed that the elastic stiffness coefficients C44, C55 and C66 play a primary role in the slow variation of the shear modulus.

cond-mat.mtrl-sci↗

arXiv2019

Gluing two affine Yangians of $\mathfrak{gl}_1$

We construct a four-parameter family of affine Yangian algebras by gluing two copies of the affine Yangian of $\mathfrak{gl}_1$. Our construction allows for gluing operators with arbitrary (integer or half integer) conformal dimension and arbitrary (bosonic or fermionic) statistics, which is related to the relative framing. The resulting family of algebras is a two-parameter generalization of the $\mathcal{N}=2$ affine Yangian, which is isomorphic to the universal enveloping algebra of $\mathfrak{u}(1)\oplus \mathcal{W}^{\mathcal{N}=2}_{\infty}[λ]$. All algebras that we construct have natural representations in terms of "twin plane partitions", a pair of plane partitions appropriately joined along one common leg. We observe that the geometry of twin plane partitions, which determines the algebra, bears striking similarities to the geometry of certain toric Calabi-Yau threefolds.

hep-th↗

arXiv2019

Band gap evolution in Ruddlesden-Popper phases

We investigate the variation of the band gap across the Ruddlesden-Popper (RP) series (An+1BnX3n+1) in model chalcogenide, oxide, and halide materials to understand the factors influencing band gap evolution. In contrast to the oxides and halides, we find the band gap of the chalcogenides evolve differently with the thickness of the perovskite blocks in these natural superlattices. We show that octahedral rotations (i.e. deviation of the B-X-B bond angles from 180) and quantum confinement effects compete to decide the band gap evolution of RP phases. The insights gained here will allow us to rationally design layered perovskite phases for electronics and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2019

Experimental Gaussian Boson Sampling

Gaussian Boson sampling (GBS) provides a highly efficient approach to make use of squeezed states from parametric down-conversion to solve a classically hard-to-solve sampling problem. The GBS protocol not only significantly enhances the photon generation probability, compared to standard boson sampling with single photon Fock states, but also links to potential applications such as dense subgraph problems and molecular vibronic spectra. Here, we report the first experimental demonstration of GBS using squeezed-state sources with simultaneously high photon indistinguishability and collection efficiency. We implement and validate 3-, 4- and 5-photon GBS with high sampling rates of 832 kHz, 163 kHz and 23 kHz, respectively, which is more than 4.4, 12.0, and 29.5 times faster than the previous experiments. Further, we observe a quantum speed-up on a NP-hard optimization problem when comparing with simulated thermal sampler and uniform sampler.

quant-ph↗

arXiv2019

High-Resolution Network for Photorealistic Style Transfer

Photorealistic style transfer aims to transfer the style of one image to another, but preserves the original structure and detail outline of the content image, which makes the content image still look like a real shot after the style transfer. Although some realistic image styling methods have been proposed, these methods are vulnerable to lose the details of the content image and produce some irregular distortion structures. In this paper, we use a high-resolution network as the image generation network. Compared to other methods, which reduce the resolution and then restore the high resolution, our generation network maintains high resolution throughout the process. By connecting high-resolution subnets to low-resolution subnets in parallel and repeatedly multi-scale fusion, high-resolution subnets can continuously receive information from low-resolution subnets. This allows our network to discard less information contained in the image, so the generated images may have a more elaborate structure and less distortion, which is crucial to the visual quality. We conducted extensive experiments and compared the results with existing methods. The experimental results show that our model is effective and produces better results than existing methods for photorealistic image stylization. Our source code with PyTorch framework will be publicly available at https://github.com/limingcv/Photorealistic-Style-Transfer

cs.CV↗

arXiv2019

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items. The matching stage retrieves candidate items relevant to user interests, while the ranking stage sorts candidate items by user interests. Thus, the most critical ability is to model and represent user interests for either stage. Most of the existing deep learning-based models represent one user as a single vector which is insufficient to capture the varying nature of user's interests. In this paper, we approach this problem from a different view, to represent one user with multiple vectors encoding the different aspects of the user's interests. We propose the Multi-Interest Network with Dynamic routing (MIND) for dealing with user's diverse interests in the matching stage. Specifically, we design a multi-interest extractor layer based on capsule routing mechanism, which is applicable for clustering historical behaviors and extracting diverse interests. Furthermore, we develop a technique named label-aware attention to help learn a user representation with multiple vectors. Through extensive experiments on several public benchmarks and one large-scale industrial dataset from Tmall, we demonstrate that MIND can achieve superior performance than state-of-the-art methods for recommendation. Currently, MIND has been deployed for handling major online traffic at the homepage on Mobile Tmall App.

cs.IR↗

arXiv2019

Thermal Tensor Renormalization Group Simulations of Square-Lattice Quantum Spin Models

In this work, we benchmark the well-controlled and numerically accurate exponential thermal tensor renormalization group (XTRG) in the simulation of interacting spin models in two dimensions. Finite temperature introduces a thermal correlation length, which justifies the analysis of finite system size for the sake of numerical efficiency. In this paper we focus on the square lattice Heisenberg antiferromagnet (SLH) and quantum Ising models (QIM) on open and cylindrical geometries up to width $W=10$. We explore various one-dimensional mapping paths in the matrix product operator (MPO) representation, whose performance is clearly shown to be geometry dependent. We benchmark against quantum Monte Carlo (QMC) data, yet also the series-expansion thermal tensor network results. Thermal properties including the internal energy, specific heat, and spin structure factors, etc., are computed with high precision, obtaining excellent agreement with QMC results. XTRG also allows us to reach remarkably low temperatures. For SLH we obtain at low temperature an energy per site $u_g^*\simeq -0.6694(4)$ and a spontaneous magnetization $m_S^*\simeq0.30(1)$, which is already consistent with the ground state properties. We extract an exponential divergence vs. $T$ of the structure factor $S(M)$, as well as the correlation length $ξ$, at the ordering wave vector $M=(π,π)$, which represents the renormalized classical behavior and can be observed over a narrow but appreciable temperature window, by analysing the finite-size data by XTRG simulations. For the QIM with a finite-temperature phase transition, we employ several thermal quantities, including the specific heat, Binder ratio, as well as the MPO entanglement to determine the critical temperature $T_c$.

cond-mat.str-el↗

arXiv2019

Two-temperature scales in the triangular-lattice Heisenberg antiferromagnet

The anomalous thermodynamic properties of the paradigmatic frustrated spin-1/2 triangular lattice Heisenberg antiferromagnet (TLH) has remained an open topic of research over decades, both experimentally and theoretically. Here we further the theoretical understanding based on the recently developed, powerful exponential tensor renormalization group (XTRG) method on cylinders and stripes in a quasi one-dimensional (1D) setup, as well as a tensor product operator approach directly in 2D. The observed thermal properties of the TLH are in excellent agreement with two recent experimental measurements on the virtually ideal TLH material Ba$_8$CoNb$_6$O$_{24}$. Remarkably, our numerical simulations reveal two crossover temperature scales, at $T_l/J \sim 0.20$ and $T_h/J\sim 0.55$, with $J$ the Heisenberg exchange coupling, which are also confirmed by a more careful inspection of the experimental data. We propose that in the intermediate regime between the low-temperature scale $T_l$ and the higher one $T_h$, the gapped "roton-like" excitations are activated with a strong chiral component and a large contribution to thermal entropies, which suppress the incipient 120$^\circ$ order that emerges for temperatures below $T_l$.

cond-mat.str-el↗

arXiv2019

Primal Meaning Recommendation via On-line Encyclopedia

Polysemy is a very common phenomenon in modern languages. Under many circumstances, there exists a primal meaning for the expression. We define the primal meaning of an expression to be a frequently used sense of that expression from which its other frequent senses can be deduced. Many of the new appearing meanings of the expressions are either originated from a primal meaning, or are merely literal references to the original expression, e.g., apple (fruit), Apple (Inc), and Apple (movie). When constructing a knowledge base from on-line encyclopedia data, it would be more efficient to be aware of the information about the importance of the senses. In this paper, we would like to explore a way to automatically recommend the primal meaning of an expression based on the textual descriptions of the multiple senses of an expression from on-line encyclopedia websites. We propose a hybrid model that captures both the pattern of the description and the relationship between different descriptions with both weakly supervised and unsupervised models. The experiment results show that our method yields a good result with a P@1 (precision) score of 83.3 per cent, and a MAP (mean average precision) of 90.5 per cent, surpassing the UMFS-WE baseline by a big margin (P@1 is 61.1 per cent and MAP is 76.3 per cent).

cs.CL↗

arXiv2019

Versatile Electronic Devices Based on WSe2/SnSe2 Vertical van der Waals Heterostructure

Van der Waals heterostructures formed by stacking of various two-dimensional materials are promising in electronic applications. However, the performances of most reported electronic devices based on van der Waals heterostructures are far away from existing technologies'. Here we report high performance heterostructure devices based on vertically stacked tungsten diselenide and tin diselenide. Due to the unique band alignment and the atomic thickness of the material, both charge carrier transport and energy barrier can be effectively modulated by the applied electrical field. As a result, the heterostructure devices show superb characteristics, with a high current on-off ratio of around 3 x 108, an average subthreshold slope of 126 mV per decade over five decades of current change due to band to band tunneling, an ultra-high rectification ratio of around 3 x 108 and a current density of more than 104 A per cm2. Further, a small signal half wave rectifier circuit based on majority carrier transport dominated diode is successfully demonstrated, showing great potential in future high speed electronic applications.

cond-mat.mes-hall↗

arXiv2019

Single-crystal growth and extremely high H_c2 of 12442-type Fe-based superconductor KCa_2Fe_4As_4F_2

Millimeter sized single crystals of KCa_2Fe_4As_4F_2 were grown using a self-flux method. The chemical compositions and crystal structure were characterized carefully. Superconductivity with the critical transition T_c = 33.5 K was confirmed by both the resistivity and magnetic susceptibility measurements. Moreover, the upper critical field H_c2 was studied by the resistivity measurements under different magnetic fields. A rather steep increase for the in-plane H_c2^ab with cooling, dμ_0H_c2^a/dT|T_c = -50.9 T/K, was observed, indicating an extremely high upper critical field. Possible origins for this behavior were discussed. The findings in our work is a great promotion both for understanding the physical properties and applications of 12442-type Fe-based superconductors.

cond-mat.supr-con↗

arXiv2019

Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.

cs.CV↗

arXiv2019

Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy. The codes, including the extension, are available at https://github.com/lancopku/meSimp

cs.LG↗

arXiv2019

Embedded eigenvalues for the Neumann-Poincaré operator

The Neumann-Poincaré operator is a boundary-integral operator associated with harmonic layer potentials. This article proves the existence of eigenvalues within the essential spectrum for the Neumann-Poincaré operator for certain Lipschitz curves in the plane with reflectional symmetry, when considered in the functional space in which it is self-adjoint. The proof combines the compactness of the Neumann-Poincaré operator for curves of class $C^{2,α}$ with the essential spectrum generated by a corner. Eigenvalues corresponding to even (odd) eigenfunctions are proved to lie within the essential spectrum of the odd (even) component of the operator when a $C^{2,α}$ curve is perturbed by inserting a small corner.

math.SP↗

arXiv2019

Tuning parameter calibration for $\ell_1$-regularized logistic regression

Feature selection is a standard approach to understanding and modeling high-dimensional classification data, but the corresponding statistical methods hinge on tuning parameters that are difficult to calibrate. In particular, existing calibration schemes in the logistic regression framework lack any finite sample guarantees. In this paper, we introduce a novel calibration scheme for $\ell_1$-penalized logistic regression. It is based on simple tests along the tuning parameter path and is equipped with optimal guarantees for feature selection. It is also amenable to easy and efficient implementations, and it rivals or outmatches existing methods in simulations and real data applications.

stat.ME↗

arXiv2019

General framework for testing Poisson-Voronoi assumption for real microstructures

Modeling microstructures is an interesting problem not just in Materials Science but also in Mathematics and Statistics. The most basic model for steel microstructure is the Poisson-Voronoi diagram. It has mathematically attractive properties and it has been used in the approximation of single phase steel microstructures. The aim of this paper is to develop methods that can be used to test whether a real steel microstructure can be approximated by such a model. Therefore, a general framework for testing the Poisson-Voronoi assumption based on images of 2D sections of real metals is set out. Following two different approaches, according to the use or not of periodic boundary conditions, three different model tests are proposed. The first two are based on the coefficient of variation and the cumulative distribution function of the cells area. The third exploits tools from to Topological Data Analysis, such as persistence landscapes.

stat.AP↗