arXiv subjects

Jianfeng Wang

Publications and source records attributed to Jianfeng Wang.

At least 163 records · Page 9Linked to original sources

arXiv2018

SFace: An Efficient Network for Face Detection in Large Scale Variations

Face detection serves as a fundamental research topic for many applications like face recognition. Impressive progress has been made especially with the recent development of convolutional neural networks. However, the issue of large scale variations, which widely exists in high resolution images/videos, has not been well addressed in the literature. In this paper, we present a novel algorithm called SFace, which efficiently integrates the anchor-based method and anchor-free method to address the scale issues. A new dataset called 4K-Face is also introduced to evaluate the performance of face detection with extreme large scale variations. The SFace architecture shows promising results on the new 4K-Face benchmarks. In addition, our method can run at 50 frames per second (fps) with an accuracy of 80% AP on the standard WIDER FACE dataset, which outperforms the state-of-art algorithms by almost one order of magnitude in speed while achieves comparative performance.

cs.CV↗

arXiv2017

Giant Enhancement of Intrinsic Spin Hall Conductivity in $β$ Tungsten via Substitutional Doping

A key challenge in manipulating the magnetization in heavy-metal/ferromagnetic bilayers via the spin-orbit torque is to identify materials that exhibit an efficient charge-to-spin current conversion. Ab initio electronic structure calculations reveal that the intrinsic spin Hall conductivity (SHC) for pristine $β$-W is about sixty percent larger than that of $α$-W. More importantly, we demonstrate that the SHC of $β$-W can be enhanced via Ta alloying. This is corroborated by spin Berry curvature calculations of W$_{1-x}$Ta$_x$ ($x$ $\sim$ 12.5%) alloys which show a giant enhancement of spin Hall angle of up to $\approx$ $-0.5$. The underlying mechanism is the synergistic behavior of the SHC and longitudinal conductivity with Fermi level position. These findings, not only pave the way for enhancing the intrinsic spin Hall effect in $β$-W, but also provide new guidelines to exploit substitutional alloying to tailor the spin Hall effect in various materials.

cond-mat.mtrl-sci↗

arXiv2017

Face Attention Network: An Effective Face Detector for the Occluded Faces

The performance of face detection has been largely improved with the development of convolutional neural network. However, the occlusion issue due to mask and sunglasses, is still a challenging problem. The improvement on the recall of these occluded cases usually brings the risk of high false positives. In this paper, we present a novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed. More specifically, we propose a new anchor-level attention, which will highlight the features from the face region. Integrated with our anchor assign strategy and data augmentation techniques, we obtain state-of-art results on public face detection benchmarks like WiderFace and MAFA. The code will be released for reproduction.

cs.CV↗

arXiv2017

Sparsity estimation in compressive sensing with application to MR images

The theory of compressive sensing (CS) asserts that an unknown signal $\mathbf{x} \in \mathbb{C}^N$ can be accurately recovered from $m$ measurements with $m\ll N$ provided that $\mathbf{x}$ is sparse. Most of the recovery algorithms need the sparsity $s=\lVert\mathbf{x}\rVert_0$ as an input. However, generally $s$ is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simulation study and real data study, magnetic resonance image data is used as input signal, which becomes sparse after sparsified transformation. The results from the simulation study confirm the theoretical properties of the estimator. In practice, the estimate from a real MR image can be used for recovering future MR images under the framework of CS if they are believed to have the same sparsity level after sparsification.

stat.ME↗

arXiv2017

Prediction of Ideal Topological Semimetals with Triply Degenerate Points in NaCu$_3$Te$_2$ Family

Triply degenerate points (TDPs) in band structure of a crystal can generate novel TDP fermions without high-energy counterparts. Although identifying ideal TDP semimetals, which host clean TDP fermions around the Fermi level ($E_F$) without coexisting of other quasiparticles, is critical to explore the intrinsic properties of this new fermion, it is still a big challenge and has not been achieved up to now. Here, we disclose an effective approach to search for ideal TDP semimetals via selective band crossing between antibonding $s$ and bonding $p$ orbitals along a line in the momentum space with $C_{3v}$ symmetry. Applying this approach, we have successfully identified the NaCu$_3$Te$_2$ family of compounds to be ideal TDP semimetals, where two and only two pairs of TDPs are located around the $E_F$. Moreover, we reveal an interesting mechanism to modulate energy splitting between a pair of TDPs, and illustrate the intrinsic features of TDP Fermi arcs in these ideal TDP semimetals.

cond-mat.mtrl-sci↗

arXiv2017

The first order partial differential equations resolved with any derivatives

In this paper we discuss the first order partial differential equations resolved with any derivatives. At first, we transform the first order partial differential equation resolved with respect to a time derivative into a system of linear equations. Secondly, we convert it into a system of the first order linear partial differential equations with constant coefficients and nonlinear algebraic equations. Thirdly, we solve them by the Fourier transform and convert them into the equivalent integral equations. At last, we extend to discuss the first order partial differential equations resolved with respect to time derivatives and the general scenario resolved with any derivatives.

math.AP↗

arXiv2017

Voltage-Controllable Colossal Magnetocrystalline Anisotropy in Single Layer Transition Metal Dichalcogenides

Materials with large magnetocrystalline anisotropy and strong electric field effects are highly needed to develop new types of memory devices based on electric field control of spin orientations. Instead of using modified transition metal films, we propose that certain monolayer transition metal dichalcogenides are the ideal candidate materials for this purpose. Using density functional calculations, we show that they exhibit not only a large magnetocrystalline anisotropy (MCA), but also colossal voltage modulation under external field. Notably, in some materials like CrSe_2 and FeSe_2, where spins show a strong preference for in-plane orientation, they can be switched to out-of-plane direction. This effect is attributed to the large band character alteration that the transition metal d-states undergo around the Fermi energy due to the electric field. We further demonstrate that strain can also greatly change MCA, and can help to improve the modulation efficiency while combined with an electric field.

cond-mat.mtrl-sci↗

arXiv2017

Two bosons in a narrowly resonant trap

It is rare for an $S$-wave resonance to remain narrow while approaching threshold as a result of the interaction parameters being fine tuned. Such an interaction is, however, realized by pion-charmed baryon system $πΣ_c$, which has a narrow resonance $Λ_c^+(2595)$ located a few MeVs above its threshold. We study the consequence of narrowly resonant inter-species interaction in a three-body system composed of two identical light bosons and an extremely heavy particle. We concern ourselves in the present paper with resonances of the three-body system, as opposed to bound states. The focus is on the technical aspects of constructing the integral equation for three-body amplitudes and of analytically continuing the integral equation to locate the "trimer" resonance pole in the unphysical sheet of the center-of-mass energy plane.

nucl-th↗

arXiv2017

Random number generation with cosmic photons

Random numbers are indispensable for a variety of applications ranging from testing physics foundation to information encryption. In particular, nonlocality tests provide a strong evidence to our current understanding of nature -- quantum mechanics. All the random number generators (RNG) used for the existing tests are constructed locally, making the test results vulnerable to the freedom-of-choice loophole. We report an experimental realization of RNGs based on the arrival time of cosmic photons. The measurement outcomes (raw data) pass the standard NIST statistical test suite. We present a realistic design to employ these RNGs in a Bell test experiment, which addresses the freedom-of-choice loophole.

quant-ph↗

arXiv2017

Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI

The purpose of this study is to investigate a method, using simulations, to improve contrast agent quantification in Dynamic Contrast Enhanced MRI. Bayesian hierarchical models (BHMs) are applied to smaller images ($10\times10\times10$) such that spatial information can be incorporated. Then exploratory analysis is done for larger images ($64\times64\times64$) by using maximum a posteriori (MAP). For smaller images: the estimators of proposed BHMs show improvements in terms of the root mean squared error compared to the estimators in existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperforms Besag models. For larger images: MAP estimators also show improvements by assigning Leroux prior.

stat.AP↗

arXiv2016

Electronic properties of SnTe-class topological crystalline insulator materials

The rise of topological insulators in recent years has broken new ground both in the conceptual cognition of condensed matter physics and the promising revolution of the electronic devices. It also stimulates the explorations of more topological states of matter. Topological crystalline insulator is a new topological phase, which combines the electronic topology and crystal symmetry together. In this article, we review the recent progress in the studies of SnTe-class topological crystalline insulator materials. Starting from the topological identifications in the aspects of the bulk topology, surface states calculations and experimental observations, we present the electronic properties of topological crystalline insulators under various perturbations, including native defect, chemical doping, strain, and thickness-dependent confinement effects, and then discuss their unique quantum transport properties, such as valley-selective filtering and helicity-resolved functionalities for Dirac fermions. The rich properties and high tunability make SnTe-class materials promising candidates for novel quantum devices.

cond-mat.mtrl-sci↗

arXiv2016

Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition

Detecting and recognizing objects interacting with humans lie in the center of first-person (egocentric) daily activity recognition. However, due to noisy camera motion and frequent changes in viewpoint and scale, most of the previous egocentric action recognition methods fail to capture and model highly discriminative object features. In this work, we propose a novel pipeline for first-person daily activity recognition, aiming at more discriminative object feature representation and object-motion feature fusion. Our object feature extraction and representation pipeline is inspired by the recent success of object hypotheses and deep convolutional neural network based detection frameworks. Our key contribution is a simple yet effective manipulated object proposal generation scheme. This scheme leverages motion cues such as motion boundary and motion magnitude (in contrast, camera motion is usually considered as "noise" for most previous methods) to generate a more compact and discriminative set of object proposals, which are more closely related to the objects which are being manipulated. Then, we learn more discriminative object detectors from these manipulated object proposals based on region-based convolutional neural network (R-CNN). Meanwhile, we develop a network based feature fusion scheme which better combines object and motion features. We show in experiments that the proposed framework significantly outperforms the state-of-the-art recognition performance on a challenging first-person daily activity benchmark.

cs.CV↗

arXiv2016

The Xinglong 2.16-m Telescope: Current Instruments and Scientific Projects

The Xinglong 2.16-m reflector is the first 2-meter class astronomical telescope in China. It was jointly designed and built by the Nanjing Astronomical Instruments Factory (NAIF), Beijing Astronomical Observatory (now National Astronomical Observatories, Chinese Academy of Sciences, NAOC) and Institute of Automation, Chinese Academy of Sciences in 1989. It is Ritchey-Chrétien (R-C) reflector on an English equatorial mount and the effective aperture is 2.16 meters. It had been the largest optical telescope in China for $\sim18$ years until the Guoshoujing Telescope (also called Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST) and the Lijiang 2.4-m telescope were built. At present, there are three main instruments on the Cassegrain focus available: the Beijing Faint Object Spectrograph and Camera (BFOSC) for direct imaging and low resolution ($R\sim500-2000$) spectroscopy, the spectrograph made by Optomechanics Research Inc. (OMR) for low resolution spectroscopy (the spectral resolutions are similar to those of BFOSC) and the fiber-fed High Resolution Spectrograph (HRS, $R\sim30000-65000$). The telescope is widely open to astronomers all over China as well as international astronomical observers. Each year there are more than 40 ongoing observing projects, including 6-8 key projects. Recently, some new techniques and instruments (e.g., astro-frequency comb calibration system, polarimeter and adaptive optics) have been or will be tested on the telescope to extend its observing abilities.

astro-ph.IM↗

arXiv2015

Group $K$-Means

We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary. Although theoretically low approximation errors can be achieved by the global solution, an effective solution has not been well studied in practice. To solve the problem, we propose a simple yet effective algorithm \textit{Group $K$-Means}. Specifically, we take each dictionary, or any two selected dictionaries, as a group of $K$-means cluster centers, and then deal with the approximation issue by minimizing the approximation errors. Besides, we propose a hierarchical initialization for such a non-convex problem. Experimental results well validate the effectiveness of the approach.

cs.CV↗

arXiv2014

On a Problem of Harary and Schwenk on Graphs with Distinct Eigenvalues

Harary and Schwenk posed the problem forty years ago: Which graphs have distinct adjacency eigenvalues? In this paper, we obtain a necessary and sufficient condition for an Hermitian matrix with simple spectral radius and distinct eigenvalues. As its application, we give an algebraic characterization to the Harary-Schwenk's problem. As an extension of their problem, we also obtain a necessary and sufficient condition for a positive semidefinite matrix with simple least eigenvalue and distinct eigenvalues, which can provide an algebraic characterization to their problem with respect to the (normalized) Laplacian matrix.

math.CO↗

arXiv2014

Optimized Cartesian $K$-Means

Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named Optimized Cartesian $K$-Means (OCKM), to better encode the data points for more accurate approximate nearest neighbor search. In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life datasets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.

cs.CV↗

arXiv2014

Randić energy and Randić eigenvalues

Let $G$ be a graph of order $n$, and $d_i$ the degree of a vertex $v_i$ of $G$. The Randić matrix ${\bf R}=(r_{ij})$ of $G$ is defined by $r_{ij} = 1 / \sqrt{d_jd_j}$ if the vertices $v_i$ and $v_j$ are adjacent in $G$ and $r_{ij}=0$ otherwise. The normalized signless Laplacian matrix $\mathcal{Q}$ is defined as $\mathcal{Q} =I+\bf{R}$, where $I$ is the identity matrix. The Randić energy is the sum of absolute values of the eigenvalues of $\bf{R}$. In this paper, we find a relation between the normalized signless Laplacian eigenvalues of $G$ and the Randić energy of its subdivided graph $S(G)$. We also give a necessary and sufficient condition for a graph to have exactly $k$ and distinct Randić eigenvalues.

math.CO↗

arXiv2014

Experimental observation of Dirac-like surface states and topological phase transition in Pb$_{1-x}$Sn$_x$Te(111) films

The surface of a topological crystalline insulator (TCI) carries an even number of Dirac cones protected by crystalline symmetry. We epitaxially grew high quality Pb$_{1-x}$Sn$_x$Te(111) films and investigated the TCI phase by in-situ angle-resolved photoemission spectroscopy. Pb$_{1-x}$Sn$_x$Te(111) films undergo a topological phase transition from trivial insulator to TCI via increasing the Sn/Pb ratio, accompanied by a crossover from n-type to p-type doping. In addition, a hybridization gap is opened in the surface states when the thickness of film is reduced to the two-dimensional limit. The work demonstrates an approach to manipulating the topological properties of TCI, which is of importance for future fundamental research and applications based on TCI.

cond-mat.mtrl-sci↗