arXiv subjects

Tao Wang

Publications and source records attributed to Tao Wang.

At least 757 records · Page 42Linked to original sources

arXiv2019

The mass dependence of structure, star formation rate, and mass assembly mode at 0.5<z< 2.5

To investigate the mass dependence of structural transformation and star formation quenching, we construct three galaxy samples using massive ($M_* > 10^{10} M_{\odot}$) red, green, and blue galaxy populations at $0.5<z<2.5$ in five 3D--{\it HST}/CANDELS fields. The structural parameters, including effective radius ($r_{\rm e}$), galaxy compactness ($Σ_{1.5}$), and second order moment of 20\% brightest pixels ($M_{20}$) are found to be correlated with stellar mass. Sérsic index ($n$), concentration ($C$), and Gini coefficient ($G$) seem to be insensitive to stellar mass. The morphological distinction between blue and red galaxies is found at a fixed mass bin, suggesting that quenching processes should be accompanied with transformations of galaxy structure and morphology. Except for $r_e$ and $Σ_{1.5}$ at high mass end, structural parameters of green galaxies are intermediate between red and blue galaxies in each stellar mass bin at $z < 2$, indicating green galaxies are at a transitional phase when blue galaxies are being quenched into quiescent statuses. The similar sizes and compactness for the blue and green galaxies at high-mass end implies that these galaxies will not appear to be significantly shrunk until they are completely quenched into red QGs. For the green galaxies at $0.5<z<1.5$, a morphological transformation sequence of bulge buildup can be seen as they are gradually shut down their star formation activities, while a faster morphological transformation is verified for the green galaxies at $1.5<z<2.5$.

astro-ph.GA↗

arXiv2019

Numerical analysis of a semilinear fractional diffusion equation

This paper considers the numerical analysis of a semilinear fractional diffusion equation with nonsmooth initial data. A new Grönwall's inequality and its discrete version are proposed. By the two inequalities, error estimates in three Sobolev norms are derived for a spatial semi-discretization and a full discretization, which are optimal with respect to the regularity of the solution. A sharp temporal error estimate on graded temporal grids is also rigorously established. In addition, the spatial accuracy $\scriptstyle O(h^2(t^{-α} + \ln(1/h)\!)\!) $ in the pointwise $ \scriptstyle L^2(Ω) $-norm is obtained for a spatial semi-discretization. Finally, several numerical results are provided to verify the theoretical results.

math.NA↗

arXiv2019

Analysis of a time-stepping discontinuous Galerkin method for fractional diffusion-wave equation with nonsmooth data

This paper analyzes a time-stepping discontinuous Galerkin method for fractional diffusion-wave problems. This method uses piecewise constant functions in the temporal discretization and continuous piecewise linear functions in the spatial discretization. Nearly optimal convergence rate with respect to the regularity of the solution is established when the source term is nonsmooth, and nearly optimal convergence rate $ \ln(1/τ)(\sqrt{\ln(1/h)} h^2+τ)$ is derived under appropriate regularity assumption on the source term. Convergence is also established without smoothness assumption on the initial value. Finally, numerical experiments are performed to verify the theoretical results.

math.NA↗

arXiv2019

When Your Friends Become Sellers: An Empirical Study of Social Commerce Site Beidian

Past few years have witnessed the emergence and phenomenal success of strong-tie based social commerce. Embedded in social networking sites, these E-Commerce platforms transform ordinary people into sellers, where they advertise and sell products to their friends and family in online social networks. These sites can acquire millions of users within a short time, and are growing fast at an accelerated rate. However, little is known about how these social commerce develop as a blend of social relationship and economic transactions. In this paper we present the first measurement study on the full-scale data of Beidian, one of the fastest growing social commerce sites in China, which involves 11.8 million users. We first analyzed the topological structure of the Beidian platform and highlighted its decentralized nature. We then studied the site's rapid growth and its growth mechanism via invitation cascade. Finally, we investigated purchasing behavior on Beidian, where we focused on user proximity and loyalty, which contributes to the site's high conversion rate. As the consequences of interactions between strong ties and economic logics, emerging social commerce demonstrates significant property deviations from all known social networks and E-Commerce in terms of network structure, dynamics and user behavior. To the best of our knowledge, this work is the first quantitative study on the network characteristics and dynamics of emerging social commerce platforms.

cs.SI↗

arXiv2019

Every toroidal graph without triangles adjacent to $5$-cycles is DP-$4$-colorable

DP-coloring, also known as correspondence coloring, is introduced by Dvo{ř}{á}k and Postle. It is a generalization of list coloring. In this paper, we show that every connected toroidal graph without triangles adjacent to $5$-cycles has minimum degree at most three unless it is a 2-connected $4$-regular graph with Euler characteristic $ε(G) = 0$. Consequently, every toroidal graph without triangles adjacent to $5$-cycles is DP-$4$-colorable. In the final, we show that every planar graph without two certain subgraphs is DP-$4$-colorable. As immediate consequences, (i) every planar graph without $3$-cycles adjacent to $4$-cycles is DP-$4$-colorable; (ii) every planar graph without $3$-cycles adjacent to $5$-cycles is DP-$4$-colorable; (iii) every planar graph without $4$-cycles adjacent to $5$-cycles is DP-$4$-colorable.

math.CO↗

arXiv2019

Trustable and Automated Machine Learning Running with Blockchain and Its Applications

Machine learning algorithms learn from data and use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the machine learning process is often difficult to automate. A unified analytical framework for trustable machine learning has been presented in the literature. It proposed building a trustable machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. In the proposed framework, a core machine learning algorithm can have three implementations: server layer implementation, streaming layer implementation, and smart contract implementation. However, there are still open questions. First, the streaming layer usually deploys on edge devices and therefore has limited memory and computing power. How can we run machine learning on the streaming layer? Second, most data that are stored on blockchain are financial transactions, for which fraud detection is often needed. However, in some applications, training data are hard to obtain. Can we build good machine learning models to do fraud detection with limited training data? These questions motivated this paper; which makes two contributions. First, it proposes training a machine learning model on the server layer and saving the model with a special binary data format. Then, the streaming layer can take this blob of binary data as input and score incoming data online. The blob of binary data is very compact and can be deployed on edge devices. Second, the paper presents a new method of synthetic data generation that can enrich the training data set. Experiments show that this synthetic data generation is very effective in applications such as fraud detection in financial data.

cs.LG↗

arXiv2019

Extreme sensitivity refractive index sensor based on lithography-free metal-dielectric cavity

The use of relatively simple structures to achieve high performance refractive index sensors has always been urgently needed. In this work, we propose a lithography-free sensing platform based on metal-dielectric cavity, the sensitivity of our device can reach 1456700 nm/RIU for solution and 1596700 nm/RIU for solid material, and the FOM can be up to 1234500 /RIU for solution and 1900800 /RIU for solid material, which both are much higher than most sensing methods. This sensor has excellent sensing performance in both TE and TM light, and suitable for integrated microfluidic channels. Our scheme uses a multi-layers structure with a 10 nm gold film sandwiched between prism and analyte, and shows a great potential for low-cost sensing with high performance.

physics.optics↗

arXiv2019

Dynamics of a micro-VCSEL operated in the threshold region under low-level optical feedback

Semiconductor lasers are notoriously sensitive to optical feedback, and their dynamics and coherence can be significantly modified through optical reinjection. We concentrate on the dynamical properties of a very small (i.e., microscale) Vertical Cavity Surface Emitting Laser (VCSEL) operated in the low coherence region between the emission of (partially) coherent pulses and ending below the accepted macroscopic lasing threshold, with the double objective of: 1. studying the feedback influence in a regime of very low energy consumption; 2. using the micro-VCSEL as a surrogate for nanolasers, where measurements can only be based on photon statistics. The experimental investigation is based on time traces and radiofrequency spectra (common for macroscale devices) and correlation functions (required at the nanoscale). Comparison of these results confirms the ability of correlation functions to satisfactorily characterize the action of feedback on the laser dynamics. Numerical predictions obtained from a previously developed, fully stochastic modeling technique provide very close agreement with the experimental observations, thus supporting the possible extension of our observations to the nanoscale.

physics.optics↗

arXiv2019

Flash: Efficient Dynamic Routing for Offchain Networks

Offchain networks emerge as a promising solution to address the scalability challenge of blockchain. Participants directly make payments through a network of payment channels without the overhead of committing onchain transactions. Routing is critical to the performance of offchain networks. Existing solutions use either static routing with poor performance or dynamic routing with high overhead to obtain the dynamic channel balance information. In this paper, we propose Flash, a new dynamic routing solution that leverages the unique characteristics of transactions in offchain networks to strike a better tradeoff between path optimality and probing overhead. By studying the traces of real offchain networks, we find that the payment sizes are heavy-tailed, and most payments are highly recurrent. Flash thus differentiates the treatment of elephant payments from mice payments. It uses a modified max-flow algorithm for elephant payments to find paths with sufficient capacity, and strategically routes the payment across paths to minimize the transaction fees. Mice payments are directly sent by looking up a routing table with a few precomputed paths to reduce probing overhead. Testbed experiments and data-driven simulations show that Flash improves the success volume of payments by up to 2.3x compared to the state-of-the-art routing algorithm.

cs.NI↗

arXiv2019

Distilling Object Detectors with Fine-grained Feature Imitation

State-of-the-art CNN based recognition models are often computationally prohibitive to deploy on low-end devices. A promising high level approach tackling this limitation is knowledge distillation, which let small student model mimic cumbersome teacher model's output to get improved generalization. However, related methods mainly focus on simple task of classification while do not consider complex tasks like object detection. We show applying the vanilla knowledge distillation to detection model gets minor gain. To address the challenge of distilling knowledge in detection model, we propose a fine-grained feature imitation method exploiting the cross-location discrepancy of feature response. Our intuition is that detectors care more about local near object regions. Thus the discrepancy of feature response on the near object anchor locations reveals important information of how teacher model tends to generalize. We design a novel mechanism to estimate those locations and let student model imitate the teacher on them to get enhanced performance. We first validate the idea on a developed lightweight toy detector which carries simplest notion of current state-of-the-art anchor based detection models on challenging KITTI dataset, our method generates up to 15% boost of mAP for the student model compared to the non-imitated counterpart. We then extensively evaluate the method with Faster R-CNN model under various scenarios with common object detection benchmark of Pascal VOC and COCO, imitation alleviates up to 74% performance drop of student model compared to teacher. Codes released at https://github.com/twangnh/Distilling-Object-Detectors

cs.CV↗

arXiv2019

Unidimensional continuous-variable measurement-device-independent quantum key distribution

Continuous-variable (CV) measurement-device-independent (MDI) quantum key distribution (QKD) is immune to imperfect detection devices, which can eliminate all kinds of attacks on practical detectors. Here we first propose a CV-MDI QKD scheme using unidimensional modulation (UD) in general phase-sensitive channels. The UD CV-MDI QKD protocol is implemented with the Gaussian modulation of a single quadrature of the coherent states prepared by two legitimate senders, aiming to simplify the implementation compared with the standard, symmetrically Gaussian-modulated CVMDI QKD protocol. Our scheme reduces the complexity of the system since it ignores the requirement in one of the quadrature modulations as well as the corresponding parameter estimations. The security of our proposed scheme is analyzed against collective attacks, and the finite-size analysis under realistic conditions is taken into account. UD CV-MDI QKD shows a comparable performance to that of its symmetrical counterpart, which will facilitate the simplification and practical implementation of the CV-MDI QKD protocols.

quant-ph↗

arXiv2019

High-Efficiency Second Harmonic Generation of Low-Temporal-Coherent Light Pulse

The nonlinear frequency conversion of low-temporal-coherent light holds a variety of applications and has attracted considerable interest. However, its physical mechanism remains relatively unexplored, and the conversion efficiency and bandwidth are extremely insufficient. Here, considering the instantaneous broadband characteristic, we establish a model of second harmonic generation (SHG) of low-temporal-coherent pulse, and reveal its differences from the coherent conditions. It is found that the second harmonic (SH) of low-temporal-coherent light is produced by not only the degenerate SH processes but also crossed sum-frequency processes. On the basis of this, we propose a method for realizing low-temporal-coherent SHG with high efficiency and broad bandwidth, and experimentally demonstrate a conversion efficiency up to 70% with a bandwidth of 3.1 THz (2.9 nm centered at 528 nm). To the best of our knowledge, this is the highest efficiency and broadest bandwidth of low-temporal-coherent SHG, and its efficiency is almost the same with that of the narrowband coherent condition. Furthermore, the spectral evolution characteristics of the broadband low-temporal-coherent pulse in SHG process are revealed in experiments, that the SH power spectral density (PSD) is proportional to the self-convolution of the fundamental wave PSD, which is greatly different from that of the coherent process. Our research opens a door for the study of the low-coherent nonlinear optical processes.

physics.optics↗

arXiv2019

High-Performance Support Vector Machines and Its Applications

The support vector machines (SVM) algorithm is a popular classification technique in data mining and machine learning. In this paper, we propose a distributed SVM algorithm and demonstrate its use in a number of applications. The algorithm is named high-performance support vector machines (HPSVM). The major contribution of HPSVM is two-fold. First, HPSVM provides a new way to distribute computations to the machines in the cloud without shuffling the data. Second, HPSVM minimizes the inter-machine communications in order to maximize the performance. We apply HPSVM to some real-world classification problems and compare it with the state-of-the-art SVM technique implemented in R on several public data sets. HPSVM achieves similar or better results.

cs.LG↗

arXiv2019

Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow

Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an important task to measure tumor responses to treatments. However, automatic segmentation is very challenging. This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm. In our method, a Normalized Gaussian Mixture Model (NGMM) is proposed and used to model the healthy brain tissues. Gaussian Bayesian Classifier is exploited to acquire a Gaussian Bayesian Brain Map (GBBM) from the test brain MR images. GBBM is further processed to initialize the 3D FVF algorithm, which segments the brain tumor. This algorithm has two major contributions. First, we present a NGMM to model healthy brains. Second, we extend our 2D FVF algorithm to 3D space and use it for brain tumor segmentation. The proposed method is validated on a publicly available dataset.

eess.IV↗

arXiv2019

A note on 'A fully parallel 3D thinning algorithm and its applications'

A 3D thinning algorithm erodes a 3D binary image layer by layer to extract the skeletons. This paper presents a correction to Ma and Sonka's thinning algorithm, A fully parallel 3D thinning algorithm and its applications, which fails to preserve connectivity of 3D objects. We start with Ma and Sonka's algorithm and examine its verification of connectivity preservation. Our analysis leads to a group of different deleting templates, which can preserve connectivity of 3D objects.

cs.CV↗

arXiv2019

Second harmonic and sum-frequency generations from a silicon metasurface integrated with a two-dimensional material

Silicon-based nonlinear metasurfaces were implemented only with third-order nonlinearity due to the crystal centrosymmetry and the efficiencies are considerably low, which hinders their practical applications with low-power lasers. Here, we propose to integrate a two-dimensional GaSe flake onto a silicon metasurface to assist high-efficiency second-order nonlinear processes, including second-harmonic generation (SHG) and sum-frequency generation (SFG). By resonantly pumping the integrated GaSe-metasurface, which supports a Fano resonance, the obtained SHG is about two orders of magnitude stronger than the third-harmonic generation from the bare silicon metasurface. In addition, thanks to the resonant field enhancement and GaSe's strong second-order nonlinearity, SHG of the integrated structure could be excited successfully with a low-power continuous-wave laser, which makes it possible to further implement SFG. The high-efficiency second-order nonlinear processes assisted by two-dimensional materials present potentials to expand silicon metasurface's functionalities in nonlinear regime.

physics.optics↗

arXiv2019

Security bound of continuous-variable measurement-device-independent quantum key distribution with imperfect phase reference calibration

Phase reference calibration is a necessary procedure in practical continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) for the need of Bell-State Measurement (BSM). However, the phase reference calibration may become imperfect in practical applications. We explored the practical security of CV-MDI-QKD with imperfect phase reference calibration under realistic conditions of lossy and noisy quantum channel. Specifically, a comprehensive framework is developed to model and characterize the imperfection of practical phase reference calibration operation, which is mainly caused by the non-synchronization of two remote lasers in senders. Security analysis shows that the imperfect phase reference calibration has significant side effects on the performance and security of the CV-MDI-QKD protocol. A tight security bound to excess noise introduced by imperfect phase reference calibration is derived for reverse reconciliation against arbitrary collective attacks in the asymptotic limit, and the tolerance of the CV-MDI-QKD protocol to this excess noise is also obtained. This security analysis framework can eliminate the security hazards caused by imperfect phase reference calibration without changing the existing CV-MDI-QKD system structure.

quant-ph↗

arXiv2019

Dynamics of a Ferromagnetic Particle Levitated Over a Superconductor

Under conditions where the angular momentum of a ferromagnetic particle is dominated by intrinsic spin, applied torque is predicted to cause gyroscopic precession of the particle. If the particle is sufficiently isolated from the environment, a measurement of spin precession can potentially yield sensitivity to torque beyond the standard quantum limit. Levitation of a micron-scale ferromagnetic particle above a superconductor is a possible method of near frictionless suspension enabling observation of ferromagnetic particle precession and ultrasensitive torque measurements. We experimentally investigate the dynamics of a micron-scale ferromagnetic particle levitated above a superconducting niobium surface. We find that the levitating particles are trapped in potential minima associated with residual magnetic flux pinned by the superconductor and, using an optical technique, characterize the quasiperiodic motion of the particles in these traps.

physics.app-ph↗