arXiv subjects

Jian Sun

Publications and source records attributed to Jian Sun.

At least 487 records · Page 27Linked to original sources

arXiv2015

Pressure Induced Enhancement of Superconductivity in LaRu2P2

To explore new superconductors beyond the copper-based and iron-based systems is very important. The Ru element locates just below the Fe in the periodic table and behaves like the Fe in many ways. One of the common thread to induce high temperature superconductivity is to introduce moderate correlation into the system. In this paper, we report the significant enhancement of superconducting transition temperature from 3.84K to 5.77K by using a pressure only of 1.74 GPa in LaRu2P2 which has an iso-structure of the iron-based 122 superconductors. The ab-initio calculation shows that the superconductivity in LaRu2P2 at ambient pressure can be explained by the McMillan's theory with strong electron-phonon coupling. However, it is difficult to interpret the significant enhancement of Tc versus pressure within this picture. Detailed analysis of the pressure induced evolution of resistivity and upper critical field Hc2(T) reveals that the increases of Tc with pressure may be accompanied by the involvement of extra electronic correlation effect. This suggests that the Ru-based system has some commonality as the Fe-based superconductors.

cond-mat.supr-con↗

arXiv2015

Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal

In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

cs.CV↗

arXiv2015

Sudden change of geometric quantum discord in finite temperature reservoirs

We investigate sudden change (SC) behaviors of the distance-based measures of geometric quantum discords (GQDs) for two non-interacting qubits subject to the two-sided and the one-sided thermal reservoirs. We found that the GQDs defined by different distances exhibit different SCs, and thus the SCs are the combined result of the chosen discord measure and the property of a state. We also found that the thermal reservoir may generate states having different orderings related to different GQDs. These inherent differences of the GQDs reveal that they are incompatible in characterizing quantum correlations both quantitatively and qualitatively.

quant-ph↗

arXiv2015

Convolutional Feature Masking for Joint Object and Stuff Segmentation

The topic of semantic segmentation has witnessed considerable progress due to the powerful features learned by convolutional neural networks (CNNs). The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. This strategy introduces artificial boundaries on the images and may impact the quality of the extracted features. Besides, the operations on the raw image domain require to compute thousands of networks on a single image, which is time-consuming. In this paper, we propose to exploit shape information via masking convolutional features. The proposal segments (e.g., super-pixels) are treated as masks on the convolutional feature maps. The CNN features of segments are directly masked out from these maps and used to train classifiers for recognition. We further propose a joint method to handle objects and "stuff" (e.g., grass, sky, water) in the same framework. State-of-the-art results are demonstrated on benchmarks of PASCAL VOC and new PASCAL-CONTEXT, with a compelling computational speed.

cs.CV↗

arXiv2015

Geometrically Enhanced Quantum Oscillatory Signal and Nonzero Berry's Phase in Indium Arsenide Surface

In a system accommodating both surface and bulk conduction channels, a long-standing challenge is to extract weak Shubnikov-de Haas oscillation signal in the surface from a large background stemming from the bulk. Conventional methods to suppress the bulk conduction often involve doping, an intrusive approach, to reduce the bulk carrier density. Here we propose a geometric method, i.e. attaching a metal shunt to the indium arsenide epilayer, to redistribute current and thus enhance the oscillation-to-background ratio. This allows us, for the first time, to observe clear quantum oscillations and nonzero Berry's phase at the surface of indium arsenide. We also identify the existence of a Rashba type spin-orbit interaction, on the InAs surface, with a large coupling constant ~ 1 eVA. We anticipate wide applicability of this non-intrusive architecture in similar systems such as topological insulators.

cond-mat.mes-hall↗

arXiv2015

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this work, we study rectifier neural networks for image classification from two aspects. First, we propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on our PReLU networks (PReLU-nets), we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66%). To our knowledge, our result is the first to surpass human-level performance (5.1%, Russakovsky et al.) on this visual recognition challenge.

cs.CV↗

arXiv2014

Discrete Conformal Deformation: Algorithm and Experiments

In this paper, we introduce a definition of discrete conformality for triangulated surfaces with flat cone metrics and describe an algorithm for solving the problem of prescribing curvature, that is to deform the metric discrete conformally so that the curvature of the resulting metric coincides with the prescribed curvature. We explicitly construct a discrete conformal map between the input triangulated surface and the deformed triangulated surface. Our algorithm can handle the surface with any topology with or without boundary, and can find a deformed metric for any prescribed curvature satisfying the Gauss-Bonnet formula. In addition, we present the numerical examples to show the convergence of our discrete conformality and to demonstrate the efficiency and the robustness of our algorithm.

cs.CG↗

arXiv2014

Convolutional Neural Networks at Constrained Time Cost

Though recent advanced convolutional neural networks (CNNs) have been improving the image recognition accuracy, the models are getting more complex and time-consuming. For real-world applications in industrial and commercial scenarios, engineers and developers are often faced with the requirement of constrained time budget. In this paper, we investigate the accuracy of CNNs under constrained time cost. Under this constraint, the designs of the network architectures should exhibit as trade-offs among the factors like depth, numbers of filters, filter sizes, etc. With a series of controlled comparisons, we progressively modify a baseline model while preserving its time complexity. This is also helpful for understanding the importance of the factors in network designs. We present an architecture that achieves very competitive accuracy in the ImageNet dataset (11.8% top-5 error, 10-view test), yet is 20% faster than "AlexNet" (16.0% top-5 error, 10-view test).

cs.CV↗

arXiv2014

Efficient and Accurate Approximations of Nonlinear Convolutional Networks

This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs). Unlike existing methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We minimize the reconstruction error of the nonlinear responses, subject to a low-rank constraint which helps to reduce the complexity of filters. We develop an effective solution to this constrained nonlinear optimization problem. An algorithm is also presented for reducing the accumulated error when multiple layers are approximated. A whole-model speedup ratio of 4x is demonstrated on a large network trained for ImageNet, while the top-5 error rate is only increased by 0.9%. Our accelerated model has a comparably fast speed as the "AlexNet", but is 4.7% more accurate.

cs.CV↗

arXiv2014

Recommendation Scheme Based on Converging Properties for Contents Broadcasting

Popular videos are often clicked by a mount of users in a short period. With content recommendation, the popular contents could be broadcast to the potential users in wireless network, to save huge transmitting resource. In this paper, the contents propagation model is analyzed due to users' historical behavior, location, and the converging properties in wireless data transmission, with the users' communication log in the Chinese commercial cellular network. And a recommendation scheme is proposed to achieve high energy efficiency.

cs.MM↗

arXiv2014

Power Scheduling of Kalman Filtering in Wireless Sensor Networks with Data Packet Drops

For a wireless sensor network (WSN) with a large number of low-cost, battery-driven, multiple transmission power leveled sensor nodes of limited transmission bandwidth, then conservation of transmission resources (power and bandwidth) is of paramount importance. Towards this end, this paper considers the problem of power scheduling of Kalman filtering for general linear stochastic systems subject to data packet drops (over a packet-dropping wireless network). The transmission of the acquired measurement from the sensor to the remote estimator is realized by sequentially transmitting every single component of the measurement to the remote estimator in one time period. The sensor node decides separately whether to use a high or low transmission power to communicate every component to the estimator across a packet-dropping wireless network based on the rule that promotes the power scheduling with the least impact on the estimator mean squared error. Under the customary assumption that the predicted density is (approximately) Gaussian, leveraging the statistical distribution of sensor data, the mechanism of power scheduling, the wireless network effect and the received data, the minimum mean squared error estimator is derived. By investigating the statistical convergence properties of the estimation error covariance, we establish, for general linear systems, both the sufficient condition and the necessary condition guaranteeing the stability of the estimator.

eess.SY↗

arXiv2014

A discrete uniformization theorem for polyhedral surfaces II

A discrete conformality for hyperbolic polyhedral surfaces is introduced in this paper. This discrete conformality is shown to be computable. It is proved that each hyperbolic polyhedral metric on a closed surface is discrete conformal to a unique hyperbolic polyhedral metric with a given discrete curvature satisfying Gauss-Bonnet formula. Furthermore, the hyperbolic polyhedral metric with given curvature can be obtained using a discrete Yamabe flow with surgery. In particular, each hyperbolic polyhedral metric on a closed surface with negative Euler characteristic is discrete conformal to a unique hyperbolic metric.

math.GT↗

arXiv2013

Disorder-Driven Superconductor-Insulator Transition in d-Wave Superconducting Ultrathin Films

We study the superconductor-insulator transition (SIT) in $d$-wave superconducting ultrathin films. By means of the kernel polynomial method, the Bogoliubov-de Gennes equations are solved for square lattices with up to $360\times 360$ unit cells self-consistently, making it possible to observe fully the nanoscale spatial fluctuations of the superconducting order parameters and discriminate accurately the localized quasiparticle states from the extended ones by the lattice-size scaling of the generalized inverse participation ratio. It is shown that Anderson localization can not entirely inhibit the occurrence of the local superconductivity in strongly-disordered $d$-wave superconductors. Separated by an insulating 'sea' completely, a few isolated superconducting 'islands' with significant enhancement of the local superconducting order parameters can survive across the SIT. The disorder-driven SIT, therefore, is a transition from a $d$-wave superconductor to a Bose insulator which consists of localized Cooper pairs. Unlike an $s$-wave superconductor which presents a robust single-particle gap across the SIT, the optical conductivity of a $d$-wave superconductor reveals a gapless insulating phase, where the SIT can be detected by observing the disappearance of the Drude weight with the increasing disorder.

cond-mat.supr-con↗

arXiv2013

Stable all nitrogen metallic salt at terapascal pressures

The phase diagram and equation of state of dense nitrogen are of interest in understanding the fundamental physics and chemistry under extreme conditions, including planetary processes, and in discovering new materials. We predict several stable phases of nitrogen at multi-TPa pressures, including a P4/nbm structure consisting of partially charged N2 pairs and N5 tetrahedra, which is stable in the range 2.5-6.8 TPa. This is followed by a modulated layered structure between 6.8 and 12.6 TPa, which also exhibits a significant charge transfer. The P4/nbm metallic nitrogen salt and the modulated structure are stable at high pressures and temperatures, and they exhibit strongly ionic features and charge density distortions, which is unexpected in an element under such extreme conditions and could represent a new class of nitrogen materials. The P-T phase diagram of nitrogen at TPa pressures is investigated using quasiharmonic phonon calculations and ab initio molecular dynamics simulations.

cond-mat.mtrl-sci↗

arXiv2013

A discrete uniformization theorem for polyhedral surfaces

A discrete conformality for polyhedral metrics on surfaces is introduced in this paper which generalizes earlier work on the subject. It is shown that each polyhedral metric on a surface is discrete conformal to a constant curvature polyhedral metric which is unique up to scaling. Furthermore, the constant curvature metric can be found using a discrete Yamabe flow with surgery.

math.GT↗

arXiv2013

A Variational Principle for Improving 2D Triangle Meshes based on Hyperbolic Volume

In this paper, we consider the problem of improving 2D triangle meshes tessellating planar regions. We propose a new variational principle for improving 2D triangle meshes where the energy functional is a convex function over the angle structures whose maximizer is unique and consists only of equilateral triangles. This energy functional is related to hyperbolic volume of ideal 3-simplex. Even with extra constraints on the angles for embedding the mesh into the plane and preserving the boundary, the energy functional remains well-behaved. We devise an efficient algorithm for maximizing the energy functional over these extra constraints. We apply our algorithm to various datasets and compare its performance with that of CVT. The experimental results show that our algorithm produces the meshes with both the angles and the aspect ratios of triangles lying in tighter intervals.

cs.CG↗

arXiv2013

Rigidity of Infinite Hexagonal Triangulation of the Plane

In the paper, we consider the rigidity problem of the infinite hexagonal triangulation of the plane under the piecewise linear conformal changes introduced by Luo in [5]. Our result shows that if a geometric hexagonal triangulation of the plane is PL conformal to the regular hexagonal triangulation and all inner angles are in $[δ, π/2 -δ]$ for any constant $δ> 0$, then it is the regular hexagonal triangulation. This partially solves a conjecture of Luo [4]. The proof uses the concept of \emph{quasi-harmonic} functions to unfold the properties of the mesh.

math.GT↗

arXiv2013

Gromov-Hausdorff Approximation of Metric Spaces with Linear Structure

In many real-world applications data come as discrete metric spaces sampled around 1-dimensional filamentary structures that can be seen as metric graphs. In this paper we address the metric reconstruction problem of such filamentary structures from data sampled around them. We prove that they can be approximated, with respect to the Gromov-Hausdorff distance by well-chosen Reeb graphs (and some of their variants) and we provide an efficient and easy to implement algorithm to compute such approximations in almost linear time. We illustrate the performances of our algorithm on a few synthetic and real data sets.

cs.CG↗