arXiv subjects

Lei Huang

Publications and source records attributed to Lei Huang.

At least 235 records · Page 13Linked to original sources

arXiv2018

An Improved Algorithm for Crossing Curved Light Surfaces: rapidly rotating pulsar magnetospheres in curved spacetime

The structure of force-free, steady and axisymmetric magnetosphere of a neutron star (NS) is governed by the Grad-Shafranov (GS) equation, which is a second-order differential equation but degrades to first-order on the light surface (LS). The key to numerically solving the GS equation is to enable magnetic field lines smoothly cross the LS, and crossing a straight LS in flat spacetime has been a well-studied problem. But the numerical algorithm implementation becomes complicate in the presence of a bent LS, e.g. in curved spacetime, since there is no suitable computation grid adapted to it. We propose to circumvent this grid-LS mismatch problem by introducing a new coordinate frame designed such that the LS in it is a straight line. As an application, we investigate the general relativistic (GR) effect in magnetosphere structure of rapidly rotating pulsars in detail, where the LS is bent towards the central NS. We split the GR effect into two parts, curvature and frame-dragging; measure each of them and examine their dependence on the NS mass and the angular velocity for pulsars embedded in aligned dipole and multipole magnetic fields. Qualitatively speaking, we find that the curvature effect compactifies the magnetic field lines near the NS, therefore reduces the open magnetic flux and the Poynting luminosity, while the frame-dragging effect contributes a minor part in shaping the magnetosphere structure but plays a role in enhancing the spacelike current generation.

astro-ph.HE↗

arXiv2018

Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory

Business Architecture (BA) plays a significant role in helping organizations understand enterprise structures and processes, and align them with strategic objectives. However, traditional BAs are represented in fixed structure with static model elements and fail to dynamically capture business insights based on internal and external data. To solve this problem, this paper introduces the graph theory into BAs with aim of building extensible data-driven analytics and automatically generating business insights. We use IBM's Component Business Model (CBM) as an example to illustrate various ways in which graph theory can be leveraged for data-driven analytics, including what and how business insights can be obtained. Future directions for applying graph theory to business architecture analytics are discussed.

cs.SE↗

arXiv2018

Block-Normalized Gradient Method: An Empirical Study for Training Deep Neural Network

In this paper, we propose a generic and simple strategy for utilizing stochastic gradient information in optimization. The technique essentially contains two consecutive steps in each iteration: 1) computing and normalizing each block (layer) of the mini-batch stochastic gradient; 2) selecting appropriate step size to update the decision variable (parameter) towards the negative of the block-normalized gradient. We conduct extensive empirical studies on various non-convex neural network optimization problems, including multi-layer perceptron, convolution neural networks and recurrent neural networks. The results indicate the block-normalized gradient can help accelerate the training of neural networks. In particular, we observe that the normalized gradient methods having constant step size with occasionally decay, such as SGD with momentum, have better performance in the deep convolution neural networks, while those with adaptive step sizes, such as Adam, perform better in recurrent neural networks. Besides, we also observe this line of methods can lead to solutions with better generalization properties, which is confirmed by the performance improvement over strong baselines.

cs.LG↗

arXiv2018

Decorrelated Batch Normalization

Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find that PCA whitening causes a problem we call stochastic axis swapping, which is detrimental to learning. We show that ZCA whitening does not suffer from this problem, permitting successful learning. DBN retains the desirable qualities of BN and further improves BN's optimization efficiency and generalization ability. We design comprehensive experiments to show that DBN can improve the performance of BN on multilayer perceptrons and convolutional neural networks. Furthermore, we consistently improve the accuracy of residual networks on CIFAR-10, CIFAR-100, and ImageNet.

cs.CV↗

arXiv2018

Magnetar giant flares in multipolar magnetic fields. III. Multipolar magnetic field structure variations

We have analyzed the multipolar magnetic field structure variation at neutron star surface by means of the catastrophic eruption model, and find that the variation of the geometry of multipolar fields on the magnetar surface could result in the catastrophic rearrangement of the magnetosphere, which provides certain physical mechanism for the outburst of giant flares. The magnetospheric model we adopted consists of two assumptions: a helically twisted flux rope is suspended in an ideal force-free magnetosphere around the magnetar, and a current sheet emerges during the flux rope evolution. Magnetic energy accumulates during the flux rope's gradual evolution along with the variation of magnetar surface magnetic structure before the eruption. The two typical behaviors, either state transition or catastrophic escape, would take place once the flux rope loses equilibrium, thus tremendous accumulated energy is radiated. We have investigated the equilibrium state of the flux rope and the energy release affected by different multipolar structures, and find structures that could trigger violent eruption and provide the radiation approximately 0.5$\%$ of the total magnetic energy during the giant flare outburst. Our results provide certain multipolar structures of the neutron star's magnetic field with an energy release percentage 0.42$\%$ in the state transition and 0.51$\%$ in the catastrophic escape case, which are sufficient for the previously reported energy release from SGR 1806-20 giant flares.

astro-ph.HE↗

arXiv2017

DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model

Convolutional neural networks (CNNs) can be applied to graph similarity matching, in which case they are called graph CNNs. Graph CNNs are attracting increasing attention due to their effectiveness and efficiency. However, the existing convolution approaches focus only on regular data forms and require the transfer of the graph or key node neighborhoods of the graph into the same fixed form. During this transfer process, structural information of the graph can be lost, and some redundant information can be incorporated. To overcome this problem, we propose the disordered graph convolutional neural network (DGCNN) based on the mixed Gaussian model, which extends the CNN by adding a preprocessing layer called the disordered graph convolutional layer (DGCL). The DGCL uses a mixed Gaussian function to realize the mapping between the convolution kernel and the nodes in the neighborhood of the graph. The output of the DGCL is the input of the CNN. We further implement a backward-propagation optimization process of the convolutional layer by which we incorporate the feature-learning model of the irregular node neighborhood structure into the network. Thereafter, the optimization of the convolution kernel becomes part of the neural network learning process. The DGCNN can accept arbitrary scaled and disordered neighborhood graph structures as the receptive fields of CNNs, which reduces information loss during graph transformation. Finally, we perform experiments on multiple standard graph datasets. The results show that the proposed method outperforms the state-of-the-art methods in graph classification and retrieval.

cs.LG↗

arXiv2017

Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks

Orthogonal matrix has shown advantages in training Recurrent Neural Networks (RNNs), but such matrix is limited to be square for the hidden-to-hidden transformation in RNNs. In this paper, we generalize such square orthogonal matrix to orthogonal rectangular matrix and formulating this problem in feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent Stiefel Manifolds (OMDSM). We show that the rectangular orthogonal matrix can stabilize the distribution of network activations and regularize FNNs. We also propose a novel orthogonal weight normalization method to solve OMDSM. Particularly, it constructs orthogonal transformation over proxy parameters to ensure the weight matrix is orthogonal and back-propagates gradient information through the transformation during training. To guarantee stability, we minimize the distortions between proxy parameters and canonical weights over all tractable orthogonal transformations. In addition, we design an orthogonal linear module (OLM) to learn orthogonal filter banks in practice, which can be used as an alternative to standard linear module. Extensive experiments demonstrate that by simply substituting OLM for standard linear module without revising any experimental protocols, our method largely improves the performance of the state-of-the-art networks, including Inception and residual networks on CIFAR and ImageNet datasets. In particular, we have reduced the test error of wide residual network on CIFAR-100 from 20.04% to 18.61% with such simple substitution. Our code is available online for result reproduction.

cs.LG↗

arXiv2017

Projection Based Weight Normalization for Deep Neural Networks

Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.

cs.LG↗

arXiv2017

Uplink Non-Orthogonal Multiple Access with Finite-Alphabet Inputs

This paper focuses on the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. We consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, the sizes of which are assumed to be not necessarily identical. We propose to maximize the minimum Euclidean distance of the received sum-constellation with a maximum likelihood (ML) detector by adjusting the scaling factors (i.e., instantaneous transmitted powers and phases) of both users. The formulated problem is a mixed continuous-discrete optimization problem, which is nontrivial to resolve in general. By carefully observing the structure of the objective function, we discover that Farey sequence can be applied to tackle the formulated problem. However, the existing Farey sequence is not applicable when the constellation sizes of the two users are not the same. Motivated by this, we define a new type of Farey sequence, termed punched Farey sequence. Based on this, we manage to achieve a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all the possible intervals. The resulting sum-constellation is proved to be a regular QAM constellation of a larger size. Moreover, the superiority of NOMA over time-division multiple access (TDMA) in terms of minimum Euclidean distance is rigorously proved. Furthermore, the optimal rate allocation among the two users is obtained in closed-form to further maximize the obtained minimum Euclidean distance of the received signal subject to a total rate constraint. Finally, simulation results are provided to verify our theoretical analysis and demonstrate the merits of the proposed NOMA over existing orthogonal and non-orthogonal designs.

cs.IT↗

arXiv2017

On Non-Orthogonal Multiple Access with Finite-Alphabet Inputs in Z-Channels

This paper focuses on the design of non-orthogonal multiple access (NOMA) in a classical two-transmitter two-receiver Z-channel, wherein one transmitter sends information to its intended receiver from the direct link while the other transmitter sends information to both receivers from the direct and cross links. Unlike most existing designs using (continuous) Gaussian input distribution, we consider the practical finite-alphabet (i.e., discrete) inputs by assuming that the widely-used quadrature amplitude modulation (QAM) constellations are adopted by both transmitters. To balance the error performance of two receivers, we apply the max-min fairness design criterion in this paper. More specifically, we propose to jointly optimize the scaling factors at both transmitters, which control the minimum Euclidean distance of transmitting constellations, to maximize the smaller minimum Euclidean distance of two resulting constellations at the receivers, subject to an individual average power constraint at each transmitter. The formulated problem is a mixed continuous-discrete optimization problem and is thus intractable in general. By resorting to the Farey sequence, we manage to attain the closed-form expression for the optimal solution to the formulated problem. This is achieved by dividing the overall feasible region of the original optimization problem into a finite number of sub-intervals and deriving the optimal solution in each sub-interval. Through carefully observing the structure of the optimal solutions in all sub-intervals, we obtain compact and closed-form expressions for the optimal solutions to the original problem in three possible scenarios defined by the relative strength of the cross link. Simulation studies are provided to validate our analysis and demonstrate the merits of the proposed design over existing orthogonal or non-orthogonal schemes.

cs.IT↗

arXiv2017

Inexact Alternating Optimization for Phase Retrieval In the Presence of Outliers

Phase retrieval has been mainly considered in the presence of Gaussian noise. However, the performance of the algorithms proposed under the Gaussian noise model severely degrades when grossly corrupted data, i.e., outliers, exist. This paper investigates techniques for phase retrieval in the presence of heavy-tailed noise -- which is considered a better model for situations where outliers exist. An $\ell_p$-norm ($0<p<2$) based estimator is proposed for fending against such noise, and two-block inexact alternating optimization is proposed as the algorithmic framework to tackle the resulting optimization problem. Two specific algorithms are devised by exploring different local approximations within this framework. Interestingly, the core conditional minimization steps can be interpreted as iteratively reweighted least squares and gradient descent. Convergence properties of the algorithms are discussed, and the Cramér-Rao bound (CRB) is derived. Simulations demonstrate that the proposed algorithms approach the CRB and outperform state-of-the-art algorithms in heavy-tailed noise.

cs.IT↗

arXiv2017

Basis-free Solution to General Linear Quaternionic Equation

A linear quaternionic equation in one quaternionic variable q is of the form $a_1 q b_1+a_2 q b_2+ ... +a_m q b_m = c$, where the $a_i, b_j, c$ are given quaternionic coefficients. If introducing basis elements $\bf i, j, k$ of pure quaternions, then the quaternionic equation becomes four linear equations in four unknowns over the reals, and solving such equations is trivial. On the other hand, finding a quaternionic rational function expression of the solution that involves only the input quaternionic coefficients and their conjugates, called a basis-free solution, is non-trivial. In 1884, Sylvester initiated the study of basis-free solution to linear quaternionic equation. He considered the three-termed equation $aq+qb=c$, and found its solution $q=(a^2+b\overline{b}+a(b+\overline{b}))^{-1}(ac+c\overline{b})$ by successive left and right multiplications. In 2013, Schwartz extended the technique to the four-termed equation, and obtained the basis-free solution in explicit form. This paper solves the general problem for arbitrary number of terms in the non-degenerate case.

math.RA↗

arXiv2017

Micro Fourier Transform Profilometry ($μ$FTP): 3D shape measurement at 10,000 frames per second

Recent advances in imaging sensors and digital light projection technology have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with improved resolution and accuracy. However, due to the large number of projection patterns required for phase recovery and disambiguation, the maximum fame rates of current 3D shape measurement techniques are still limited to the range of hundreds of frames per second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro Fourier Transform Profilometry ($μ$FTP), which can capture 3D surfaces of transient events at up to 10,000 fps based on our newly developed high-speed fringe projection system. Compared with existing techniques, $μ$FTP has the prominent advantage of recovering an accurate, unambiguous, and dense 3D point cloud with only two projected patterns. Furthermore, the phase information is encoded within a single high-frequency fringe image, thereby allowing motion-artifact-free reconstruction of transient events with temporal resolution of 50 microseconds. To show $μ$FTP's broad utility, we use it to reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a flying dart, which were previously difficult or even unable to be captured with conventional approaches.

physics.ins-det↗

arXiv2017

Analytic properties of force-free jets in the Kerr spacetime -- III: uniform field solution

The structure of steady axisymmetric force-free magnetosphere of a Kerr black hole (BH) is governed by a second-order partial differential equation of $A_ϕ$ depending on two "free" functions $Ω(A_ϕ)$ and $I(A_ϕ)$, where $A_ϕ$ is the $ϕ$ component of the vector potential of the electromagnetic field, $Ω$ is the angular velocity of the magnetic field lines and $I$ is the poloidal electric current. In this paper, we investigate the solution uniqueness. Taking asymptotically uniform field as an example, analytic studies imply that there are infinitely many solutions approaching uniform field at infinity, while only a unique one is found in general relativistic magnetohydrodynamic simulations. To settle down the disagreement, we reinvestigate the structure of the governing equation and numerically solve it with given constraint condition and boundary condition. We find that the constraint condition (field lines smoothly crossing the light surface (LS)) and boundary conditions at horizon and at infinity are connected via radiation conditions at horizon and at infinity, rather than being independent. With appropriate constraint condition and boundary condition, we numerically solve the governing equation and find a unique solution. Contrary to naive expectation, our numerical solution yields a discontinuity in the angular velocity of the field lines and a current sheet along the last field line crossing the event horizon. We also briefly discuss the applicability of the perturbation approach to solving the governing equation.

astro-ph.HE↗

arXiv2016

Twist-induced Magnetosphere Reconfiguration for Intermittent Pulsars

We propose that the magnetosphere reconfiguration induced by magnetic twists in the closed field line region can account for the mode-switching of intermittent pulsars. We carefully investigate the properties of axisymmetric force-free pulsar magnetospheres with magnetic twists in closed field line region around the polar caps. The magnetosphere with twisted closed lines leads to enhanced spin-down rates. The enhancement in spin-down rate depends on the size of region with twisted closed lines. Typically, it is increased by a factor of $\sim2$, which is consistent with the intermittent pulsars' spin down behavior during the `off' and `on' states. We find there is a threshold of maximal twist angle $Δϕ_{\rm thres}\sim1$. The magnetosphere is stable only if the closed line twist angle is less than $Δϕ_{\rm thres}$. Beyond this value, the magnetosphere becomes unstable and gets untwisted. The spin-down rate would reduce to its off-state value. The quasi-periodicity in spin-down rate change can be explained by long-term activities in star's crust and the untwisting induced by MHD instability. The estimated duration time of on-state is about one week, consistent with observations. Due to the MHD instability, there exists an upper limit for the spin down ratio ($f\sim3$) between the on-state and the off-state, if the Y-point remains at the light cylinder.

astro-ph.HE↗

arXiv2016

Phase Retrieval Using Feasible Point Pursuit: Algorithms and Cramér-Rao Bound

Reconstructing a signal from squared linear (rank-one quadratic) measurements is a challenging problem with important applications in optics and imaging, where it is known as phase retrieval. This paper proposes two new phase retrieval algorithms based on non-convex quadratically constrained quadratic programming (QCQP) formulations, and a recently proposed approximation technique dubbed feasible point pursuit (FPP). The first is designed for uniformly distributed bounded measurement errors, such as those arising from high-rate quantization (B-FPP). The second is designed for Gaussian measurement errors, using a least squares criterion (LS-FPP). Their performance is measured against state-of-the-art algorithms and the Cramér-Rao bound (CRB), which is also derived here. Simulations show that LS-FPP outperforms the state-of-art and operates close to the CRB. Compact CRB expressions, properties, and insights are obtained by explicitly computing the CRB in various special cases -- including when the signal of interest admits a sparse parametrization, using harmonic retrieval as an example.

cs.IT↗

arXiv2015

Three-Dimensional Projective Geometry with Geometric Algebra

The line geometric model of 3-D projective geometry has the nice property that the Lie algebra sl(4) of 3-D projective transformations is isomorphic to the bivector algebra of CL(3,3), and line geometry is closely related to the classical screw theory for 3-D rigid-body motions. The canonical homomorphism from SL(4) to Spin(3,3) is not satisfying because it is not surjective, and the projective transformations of negative determinant do induce orthogonal transformations in the Plücker coordinate space of lines. This paper presents our contributions in developing a rigorous and convenient algebraic framework for the study of 3-D projective geometry with Clifford algebra. To overcome the unsatisfying defects of the Plücker correspondence, we propose a group Pin^{sp}(3,3) with Pin(3,3) as its normal subgroup, to quadruple-cover the group of 3-D projective transformations and polarities. We construct spinors in factored form that generate 3-D reflections and rigid-body motions, and extend screw algebra from the Lie algebra of rigid-body motions to other 6-D Lie subalgebras of sl(4), and construct the corresponding cross products and virtual works.

math.MG↗

arXiv2014

Varying-smoother models for functional responses

This paper studies estimation of a smooth function $f(t,s)$ when we are given functional responses of the form $f(t,\cdot)$ + error, but scientific interest centers on the collection of functions $f(\cdot,s)$ for different $s$. The motivation comes from studies of human brain development, in which $t$ denotes age whereas $s$ refers to brain locations. Analogously to varying-coefficient models, in which the mean response is linear in $t$, the "varying-smoother" models that we consider exhibit nonlinear dependence on $t$ that varies smoothly with $s$. We discuss three approaches to estimating varying-smoother models: (a) methods that employ a tensor product penalty; (b) an approach based on smoothed functional principal component scores; and (c) two-step methods consisting of an initial smooth with respect to $t$ at each $s$, followed by a postprocessing step. For the first approach, we derive an exact expression for a penalty proposed by Wood, and an adaptive penalty that allows smoothness to vary more flexibly with $s$. We also develop "pointwise degrees of freedom," a new tool for studying the complexity of estimates of $f(\cdot,s)$ at each $s$. The three approaches to varying-smoother models are compared in simulations and with a diffusion tensor imaging data set.

stat.ME↗