arXiv subjects

Tao Wang

Publications and source records attributed to Tao Wang.

At least 703 records · Page 39Linked to original sources

arXiv2020

Understanding the Role of Intermediaries in Online Social E-commerce: An Exploratory Study of Beidian

Social e-commerce, as a new form of social computing based marketing platforms, utilizes existing real-world social relationships for promotions and sales of products. It has been growing rapidly in recent years and attracted tens of millions of users in China. A key group of actors who enable market transactions on these platforms are intermediaries who connect producers with consumers by sharing information with and recommending products to their real-world social contacts. Despite their crucial role, the nature and behavior of these intermediaries on these social e-commerce platforms has not been systematically analyzed. Here we address this knowledge gap through a mixed method study. Leveraging 9 months' all-round behavior of about 40 million users on Beidian -- one of the largest social e-commerce sites in China, alongside with qualitative evidence from online forums and interviews, we examine characteristics of intermediaries, identify their behavioral patterns and uncover strategies and mechanisms that make successful intermediaries. We demonstrate that intermediaries on social e-commerce sites act as local trend detectors and "social grocers". Furthermore, successful intermediaries are highly dedicated whenever best sellers appear and broaden items for promotion. To the best of our knowledge, this paper presents the first large-scale analysis on the emerging role of intermediaries in social e-commerce platforms, which provides potential insights for the design and management of social computing marketing platforms.

cs.CY↗

arXiv2020

AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network

The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches. Exact probabilistic inference algorithms such as the forward-backward and Viterbi algorithms are typically applied in training and prediction stages of the CRF model. However, these algorithms require sequential computation that makes parallelization impossible. In this paper, we propose to employ a parallelizable approximate variational inference algorithm for the CRF model. Based on this algorithm, we design an approximate inference network that can be connected with the encoder of the neural CRF model to form an end-to-end network, which is amenable to parallelization for faster training and prediction. The empirical results show that our proposed approaches achieve a 12.7-fold improvement in decoding speed with long sentences and a competitive accuracy compared with the traditional CRF approach.

cs.CL↗

arXiv2020

Discontinuous Galerkin method for a distributed optimal control problem governed by a time fractional diffusion equation

This paper is devoted to the numerical analysis of a control constrained distributed optimal control problem subject to a time fractional diffusion equation with non-smooth initial data. The solutions of state and co-state are decomposed into singular and regular parts, and some growth estimates are obtained for the singular parts. Following the variational discretization concept, a full discretization is applied to the corresponding state and co-state equations by using linear conforming finite element method in space and piecewise constant discontinuous Galerkin method in time. By the growth estimates, error estimates are derived with non-smooth initial data. In particular, graded temporal grids are used to obtain the first-order temporal accuracy. Finally, numerical experiments are performed to verify the theoretical results.

math.NA↗

arXiv2020

Efficient inverse design and spectrum prediction for nanophotonic devices based on deep recurrent neural networks

In recent years, the development of nanophotonic devices has presented a revolutionary means to manipulate light at nanoscale. Recently, artificial neural networks (ANNs) have displayed powerful ability in the inverse design of nanophotonic devices. However, there is limited research on the inverse design for modeling and learning the sequence characteristics of a spectrum. In this work, we propose a novel deep learning method based on an improved recurrent neural networks to extract the sequence characteristics of a spectrum and achieve inverse design and spectrum prediction. A key feature of the network is that the memory or feedback loops it comprises allow it to effectively recognize time series data. In the context of nanorods hyperbolic metamaterials, we demonstrated the high consistency between the target spectrum and the predicted spectrum, and the network learned the deep physical relationship concerning the structural parameter changes reflected on the spectrum. Moreover, the proposed model is capable of predicting an unknown spectrum based on a known spectrum with only 0.32% mean relative error. We propose this method as an effective and accurate alternative to the application of ANNs in nanophotonics, paving way for fast and accurate design of desired devices.

physics.optics↗

arXiv2020

High-Q plasmonic crystal laser for ultra-sensitive biomolecule detection

Plasmonic lasers provide a paradigm-changing approach for the generation of coherent light at the nanoscale. In addition to the usual properties of coherent radiation, the emission of plasmonic lasers can feature high sensitivity to the surrounding environment, which makes this technology attractive for developing high-performance and highly-integrated sensing devices. Here, we investigate a plasmonic laser architecture based on a high-Q plasmonic crystal consisting of a periodic arrangement of nanoholes on a thin gold film cladded with an organic-dye-doped SiO$_2$ gain layer as the gain material. We report an extensive full-wave numerical analysis of the device's lasing performance and its application as a biochemical sensor, showing that the proposed design features excellent figures of merit for surface sensing that in principle can be over an order of magnitude larger than those of previously reported high-performance plasmonic biosensor architectures.

physics.optics↗

arXiv2020

An unnoticed side effect of electric vehicles

We illustrate that the electrification of our transport system might impose unnecessary extra congestion and delay for daily commuting passengers. By modelling travel behaviors of these passengers, it is found that more of them tend to depart at a narrower peak-hour time window. The occurrence of this shift is mainly caused by (1) the energy consumption of electric vehicles (EVs) is much lower than that of traditional vehicles and (2) the energy consumption of EVs is less sensitive to congestion than that of traditional vehicles. We further examine the role of congestion toll in minimizing the extra congestion and delay.

eess.SY↗

arXiv2020

Thickness identification of thin InSe by optical microscopy methods

Indium selenide (InSe), as a novel van der Waals layered semiconductor, has attracted a large research interest thanks to its excellent optical and electrical properties in the ultra-thin limit. Here, we discuss four different optical methods to quantitatively identify the thickness of thin InSe flakes on various substrates, such as SiO2/Si or transparent polymeric substrates. In the case of thin InSe deposited on a transparent substrate, the transmittance of the flake in the blue region of the visible spectrum can be used to estimate the thickness. For InSe supported by SiO2/Si, the thickness of the flakes can be estimated either by assessing their apparent colors or accurately analyzed using a Fresnel-law based fitting model of the optical contrast spectra. Finally, we also studied the thickness dependency of the InSe photoluminescence emission energy, which provides an additional tool to estimate the InSe thickness and it works both for InSe deposited on SiO2/Si and on a transparent polymeric substrate.

cond-mat.mtrl-sci↗

arXiv2020

Giant piezoresistive effect and strong band gap tunability in ultrathin InSe upon biaxial strain

The ultrathin nature and dangling bonds free surface of two-dimensional (2D) semiconductors allow for significant modifications of their band gap through strain engineering. Here, thin InSe photodetector devices are biaxially stretched, finding, a strong band gap tunability upon strain. The applied biaxial strain is controlled through the substrate expansion upon temperature increase and the effective strain transfer from the substrate to the thin InSe is confirmed by Raman spectroscopy. The band gap change upon biaxial strain is determined through photoluminescence measurements, finding a gauge factor of up to ~200 meV/%. We further characterize the effect of biaxial strain on the electrical properties of the InSe devices. In the dark state, a large increase of the current is observed upon applied strain which gives a piezoresistive gauge factor value of ~450-1000, ~5-12 times larger than that of other 2D materials and of state-of-the-art silicon strain gauges. Moreover, the biaxial strain tuning of the InSe band gap also translates in a strain-induced redshift of the spectral response of our InSe photodetectors with ΔEcut-off ~173 meV at a rate of ~360 meV/% of strain, indicating a strong strain tunability of the spectral bandwidth of the photodetectors.

cond-mat.mtrl-sci↗

arXiv2020

The Terwilliger algebra of the twisted Grassmann graph: the thin case

The Terwilliger algebra $T(x)$ of a finite connected simple graph $Γ$ with respect to a vertex $x$ is the complex semisimple matrix algebra generated by the adjacency matrix $A$ of $Γ$ and the diagonal matrices $E_i^*(x)=\operatorname{diag}(v_i)$ $(i=0,1,2,\dots)$, where $v_i$ denotes the characteristic vector of the set of vertices at distance $i$ from $x$. The twisted Grassmann graph $\tilde{J}_q(2D+1,D)$ discovered by Van Dam and Koolen in 2005 has two orbits of the automorphism group on its vertex set, and it is known that one of the orbits has the property that $T(x)$ is thin whenever $x$ is chosen from it, i.e., every irreducible $T(x)$-module $W$ satisfies $\dim E_i^*(x)W\leqslant 1$ for all $i$. In this paper, we determine all the irreducible $T(x)$-modules of $\tilde{J}_q(2D+1,D)$ for this "thin" case.

math.CO↗

arXiv2020

Well-Posedness of Free Boundary Problem in Non-relativistic and Relativistic Ideal Compressible Magnetohydrodynamics

We consider the free boundary problem for non-relativistic and relativistic ideal compressible magnetohydrodynamics in two and three spatial dimensions with the total pressure vanishing on the plasma--vacuum interface. We establish the local-in-time existence and uniqueness of solutions to this nonlinear characteristic hyperbolic problem under the Rayleigh--Taylor sign condition on the total pressure. The proof is based on certain tame estimates in anisotropic Sobolev spaces for the linearized problem and a modification of the Nash--Moser iteration scheme. Our result is uniform in the light speed and appears to be the first well-posedness result for the free boundary problem in ideal compressible magnetohydrodynamics with zero total pressure on the moving boundary.

math.AP↗

arXiv2020

Finding Action Tubes with a Sparse-to-Dense Framework

The task of spatial-temporal action detection has attracted increasing attention among researchers. Existing dominant methods solve this problem by relying on short-term information and dense serial-wise detection on each individual frames or clips. Despite their effectiveness, these methods showed inadequate use of long-term information and are prone to inefficiency. In this paper, we propose for the first time, an efficient framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. There are two key characteristics in this framework: (1) Both long-term and short-term sampled information are explicitly utilized in our spatiotemporal network, (2) A new dynamic feature sampling module (DTS) is designed to effectively approximate the tube output while keeping the system tractable. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets, achieving promising results that are competitive to state-of-the-art methods. The proposed sparse-to-dense strategy rendered our framework about 7.6 times more efficient than the nearest competitor.

cs.CV↗

arXiv2020

Radio Frequency Interference Mitigation based on the ArPLS and SumThreshold Method

As radio telescopes become sensitive, radio frequency interference (RFI) is more and more serious for interesting signals of radio astronomy. There exist demands for developing an automatic, accurate and efficient RFI mitigation method. Therefore, this work investigated the RFI detection algorithm. Firstly, we introduced an Asymmetrically Reweighted Penalized Least Squares (ArPLS) method to estimate baseline more accurately. After removing the estimated baseline, several novel strategies were proposed based on the SumThreshold algorithm for detecting different types of RFI. The threshold parameter in the SumThreshold can be determined automatically and adaptively. The adaptiveness is essential for reducing human interventions and the online RFI processing pipeline. Applications to FAST (Five-hundred-meter Aperture Spherical Telescope) data show that the proposed scheme based on the ArPLS and SumThreshold is superior to some typically available methods for RFI detection with respect to efficiency and performance.

astro-ph.IM↗

arXiv2020

High Accurate Time-of-Arrival Estimation with Fine-Grained Feature Generation for Internet-of-Things Applications

Conventional schemes often require extra reference signals or more complicated algorithms to improve the time-of-arrival (TOA) estimation accuracy. However, in this letter, we propose to generate fine-grained features from the full band and resource block (RB) based reference signals, and calculate the cross-correlations accordingly to improve the observation resolution as well as the TOA estimation results. Using the spectrogram-like cross-correlation feature map, we apply the machine learning technology with decoupled feature extraction and fitting to understand the variations in the time and frequency domains and project the features directly into TOA results. Through numerical examples, we show that the proposed high accurate TOA estimation with fine-grained feature generation can achieve at least 51% root mean square error (RMSE) improvement in the static propagation environments and 38 ns median TOA estimation errors for multipath fading environments, which is equivalently 36% and 25% improvement if compared with the existing MUSIC and ESPRIT algorithms, respectively.

eess.SP↗

arXiv2020

T-square resistivity without Umklapp scattering in dilute metallic Bi$_2$O$_2$Se

The electrical resistivity of Fermi liquids (FLs) displays a quadratic temperature ($T$) dependence because of electron-electron (e-e) scattering. For such collisions to decay the charge current, there are two known mechanisms: inter-band scattering (identified by Baber) and Umklapp events. However, dilute metallic strontium titanate (STO) was found to display $T^2$ resistivity in absence of either of these two mechanisms. The presence of soft phonons and their possible role as scattering centers raised the suspicion that $T$-square resistivity in STO is not due to e-e scattering. Here, we present the case of Bi$_2$O$_2$Se, a layered semiconductor with hard phonons, which becomes a dilute metal with a small single-component Fermi surface upon doping. It displays $T$-square resistivity well below the degeneracy temperature where neither Umklapp nor interband scattering is conceivable. We observe a universal scaling between the prefactor of $T^2$ resistivity and the Fermi energy, which is an extension of the Kadowaki-Woods plot to dilute metals. Our results imply the absence of a satisfactory theoretical basis for the ubiquity of e-e driven $T$-square resistivity in Fermi liquids.

cond-mat.str-el↗

arXiv2020

Classification Calibration for Long-tail Instance Segmentation

Remarkable progress has been made in object instance detection and segmentation in recent years. However, existing state-of-the-art methods are mostly evaluated with fairly balanced and class-limited benchmarks, such as Microsoft COCO dataset [8]. In this report, we investigate the performance drop phenomenon of state-of-the-art two-stage instance segmentation models when processing extreme long-tail training data based on the LVIS [5] dataset, and find a major cause is the inaccurate classification of object proposals. Based on this observation, we propose to calibrate the prediction of classification head to improve recognition performance for the tail classes. Without much additional cost and modification of the detection model architecture, our calibration method improves the performance of the baseline by a large margin on the tail classes. Codes will be available. Importantly, after the submission, we find significant improvement can be further achieved by modifying the calibration head, which we will update later.

cs.CV↗

arXiv2020

Quantum phase transition of the Bose-Hubbard model on cubic lattice with anisotropic hopping

In quantum many-body system, dimensionality plays a critical role on type of the quantum phase transition. In order to study the quantum system during dimensional crossover, we studied the Bose-Hubbard model on cubic lattice with anisotropic hopping by using the high order symbolic strong coupling expansion method. The analytic series expanded boundaries between the Mott-insulator and superfluid phase up to eighth order are calculated. The critical exponents are extracted by Padé re-summation method, which clearly shows the dimensional crossover behavior. Meanwhile, the critical points at commensurate filling can also be obtained, and they match well with the prediction of renormalization group theory. The scaling of the gap energy and whole phase diagram are given at last, and they can be taken as the benchmark for experiment and numerical simulations in the future study.

cond-mat.quant-gas↗

arXiv2020

InSe Schottky diodes based on van der Waals contacts

Two-dimensional semiconductors are excellent candidates for next-generation electronics and optoelec-tronics thanks to their electrical properties and strong light-matter interaction. To fabricate devices with optimal electrical properties, it is crucial to have both high-quality semiconducting crystals and ideal con-tacts at metal-semiconductor interfaces. Thanks to the mechanical exfoliation of van der Waals crystals, atomically-thin high-quality single-crystals can easily be obtained in a laboratory. However, conventional metal deposition techniques can introduce chemical disorder and metal-induced mid-gap states that induce Fermi level pinning and can degrade the metal-semiconductor interfaces, resulting in poorly performing devices. In this article, we explore the electrical contact characteristics of Au-InSe and graphite-InSe van der Waals contacts, obtained by stacking mechanically exfoliated InSe flakes onto pre-patterned Au or graphite electrodes without the need of lithography or metal deposition. The high quality of the metal-semiconductor interfaces obtained by van der Waals contact allows to fabricate high-quality Schottky di-odes based on the Au-InSe Schottky barrier. Our experimental observation indicates that the contact barrier at the graphite-InSe interface is negligible due to the similar electron affinity of InSe and graphite, while the Au-InSe interfaces are dominated by a large Schottky barrier.

cond-mat.mes-hall↗

arXiv2020

The role of traps in the photocurrent generation mechanism in thin In-Se photodetectors

Due to the excellent electrical transport properties and optoelectronic performance, thin indium selenide (InSe) has recently attracted attention in the field of 2D semiconducting materials. However, the mechanism behind the photocurrent generation in thin InSe photodetectors remains elusive. Here, we present a set of experiments aimed at explaining the strong scattering in the photoresponsivity values reported in the literature for thin InSe photodetectors. By performing optoelectronic measurements on thin InSe-based photodetectors operated under different environmental conditions we find that the photoresponsivity, the response time and the photocurrent power dependency are strongly correlated in this material. This observation indicates that the photogating effect plays an imporant role for thin InSe flakes, and it is the dominant mechanism in the ultra-high photoresponsivity of pristine InSe devices. In addition, when exposing the pristine InSe photodetectors to the ambient environment we observe a fast and irreversible change in the photoresponse, with a decrease in the photoresponsivity accompanied by an increase of the operating speed. We attribute this photodetector performance change (upon atmospheric exposure) to the decrease in the density of the traps present in InSe, due to the passivation of selenium vacancies by atmospheric oxygen species. This passivation is accompanied by a downward shift of the InSe Fermi level and by a decrease of the Fermi level pinning, which leads to an increase of the Schottky barrier between Au and InSe. Our study reveals the important role of traps induced by defects in tailoring the properties of devices based on 2D materials and offers a controllable route to design and functionalize thin InSe photodetectors to realize devices with either ultrahigh photoresposivity or fast operation speed.

cond-mat.mes-hall↗