Generative Adversarial Mapping Networks

Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and choosing an appropriate distance measure is very important for training the generative network. In this paper, we choose to use the maximum mean discrepancy (MMD) as the distance metric, which has several nice theoretical guarantees. In fact, generative moment matching network (GMMN) (Li, Swersky, and Zemel 2015) is such a generative model which contains only one generator network $G$ trained by directly minimizing MMD between the real and generated distributions. However, it fails to generate meaningful samples on challenging benchmark datasets, such as CIFAR-10 and LSUN. To improve on GMMN, we propose to add an extra network $F$, called mapper. $F$ maps both real data distribution and generated data distribution from the original data space to a feature representation space $\mathcal{R}$, and it is trained to maximize MMD between the two mapped distributions in $\mathcal{R}$, while the generator $G$ tries to minimize the MMD. We call the new model generative adversarial mapping networks (GAMNs). We demonstrate that the adversarial mapper $F$ can help $G$ to better capture the underlying data distribution. We also show that GAMN significantly outperforms GMMN, and is also superior to or comparable with other state-of-the-art GAN based methods on MNIST, CIFAR-10 and LSUN-Bedrooms datasets.Comment: 9 pages, 7 figure

Understanding current-driven dynamics of magnetic N\'{e}el walls in heavy metal/ferromagnetic metal/oxide trilayers

We consider analytically current-driven dynamics of magnetic N\'{e}el walls in heavy metal/ferromagnetic metal/oxide trilayers where strong spin-orbit coupling and interfacial Dzyaloshinskii-Moriya interaction (i-DMI) coexist. We show that field-like spin-orbit torque (FL-SOT) with effective field along $\mathbf{n}\times\hat{\mathbf{J}}$ ($\mathbf{n}$ being the interface normal and $\hat{\mathbf{J}}$ being the charge current direction) and i-DMI induced torque can both lead to Walker breakdown suppression meanwhile leaving the wall mobility (velocity versus current density) unchanged. However, i-DMI itself can not induce the "universal absence of Walker breakdown" (UAWB) while FL-SOT exceeding a certain threshold can. Finitely-enlarged Walker limits before UAWB are theoretically calculated and well explain existing data. In addition, change in wall mobility and even its sign-inversion can be understood only if the anti-damping-like (ADL) SOT is appended. For N\'{e}el walls in ferromagnetic-metal layer with both perpendicular and in-plane anisotropies, we have calculated the respective modifications of wall mobility under the coexistence of spin-transfer torque, SOTs and i-DMI. Analytics shows that in trilayers with perpendicular anisotropy strong enough spin Hall angle and appropriate sign of i-DMI parameter can lead to sign-inversion in wall mobility even under small enough current density, while in those with in-plane anisotropy this only occurs for current density in a specific range.Comment: 22 pages, 4 figur

Constraints on Kinematic Model from Recent Cosmic Observations: SN Ia, BAO and Observational Hubble Data

In this paper, linear first order expansion of deceleration parameter $q(z)=q_0+q_1(1-a)$ ($M_1$), constant jerk $j=j_0$ ($M_2$) and third order expansion of luminosity distance ($M_3$) are confronted with cosmic observations: SCP 307 SN Ia, BAO and observational Hubble data (OHD). Likelihood is implemented to find the best fit model parameters. All these models give the same prediction of the evolution of the universe which is undergoing accelerated expansion currently and experiences a transition from decelerated expansion to accelerated expansion. But, the transition redshift depends on the concrete parameterized form of the model assumed. $M_1$ and $M_2$ give value of transition redshift about $z_t\sim 0.6$. $M_3$ gives a larger one, say $z_t\sim 1$. The $\chi^2/dof$ implies almost the same goodness of the models. But, for its badness of evolution of deceleration parameter at high redshift $z>1$, $M_3$ can not be reliable. $M_1$ and $M_2$ are compatible with $\Lambda$CDM model at the $2\sigma$ and $1\sigma$ confidence levels respectively. $M_3$ is not compatible with $\Lambda$CDM model at $2\sigma$ confidence level. From $M_1$ and $M_2$ models, one can conclude that the cosmic data favor a cosmological model having $j_0<-1$.Comment: 9 pages, 3 figure

General planar transverse domain walls realized by optimized transverse magnetic field pulses in magnetic biaxial nanowires

We report the realization of a planar transverse domain wall (TDW) with arbitrary tilting angle in a magnetic biaxial nanowire under a transverse magnetic field (TMF) pulse with fixed strength and optimized orientation profile. We smooth any twisting in azimuthal angle plane of a TDW and thus completely decouple the polar and azimuthal degrees of freedom. The analytical differential equation that describes the polar angle distribution is then derived and the resulting solution is not a Walker-ansatz form. With this optimized TMF pulse comoving, the field-driven dynamics of the planar TDW is investigated. It turns out the comoving TMF pulse increases the wall velocity under the same axial driving field. These results will help to design a series of modern logic and memory nanodevices based on general planar TDWs.Comment: 4 pages, 2 figure

Network Decoupling: From Regular to Depthwise Separable Convolutions

Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first analyzes the mathematical relationship between regular convolutions and depthwise separable convolutions, and proves that the former one could be approximated with the latter one in closed form. We show depthwise separable convolutions are principal components of regular convolutions. And then we propose network decoupling (ND), a training-free method to accelerate convolutional neural networks (CNNs) by transferring pre-trained CNN models into the MobileNet-like depthwise separable convolution structure, with a promising speedup yet negligible accuracy loss. We further verify through experiments that the proposed method is orthogonal to other training-free methods like channel decomposition, spatial decomposition, etc. Combining the proposed method with them will bring even larger CNN speedup. For instance, ND itself achieves about 2X speedup for the widely used VGG16, and combined with other methods, it reaches 3.7X speedup with graceful accuracy degradation. We demonstrate that ND is widely applicable to classification networks like ResNet, and object detection network like SSD300

A One-Hop Information Based Geographic Routing Protocol for Delay Tolerant MANETs

Delay and Disruption Tolerant Networks (DTNs) may lack continuous network connectivity. Routing in DTNs is thus a challenge since it must handle network partitioning, long delays, and dynamic topology. Meanwhile, routing protocols of the traditional Mobile Ad hoc NETworks (MANETs) cannot work well due to the failure of its assumption that most network connections are available. In this article, a geographic routing protocol is proposed for MANETs in delay tolerant situations, by using no more than one-hop information. A utility function is designed for implementing the under-controlled replication strategy. To reduce the overheads caused by message flooding, we employ a criterion so as to evaluate the degree of message redundancy. Consequently a message redundancy coping mechanism is added to our routing protocol. Extensive simulations have been conducted and the results show that when node moving speed is relatively low, our routing protocol outperforms the other schemes such as Epidemic, Spray and Wait, FirstContact in delivery ratio and average hop count, while introducing an acceptable overhead ratio into the network.Comment: 14 page

MPAR: A Movement Pattern-Aware Optimal Routing for Social Delay Tolerant Networks

Social Delay Tolerant Networks (SDTNs) are a special kind of Delay Tolerant Network (DTN) that consists of a number of mobile devices with social characteristics. The current research achievements on routing algorithms tend to separately evaluate the available profit for each prospective relay node and cannot achieve the global optimal performance in an overall perspective. In this paper, we propose a Movement Pattern-Aware optimal Routing (MPAR) for SDTNs, by choosing the optimal relay node(s) set for each message, which eventually based on running a search algorithm on a hyper-cube solution space. Concretely, the movement pattern of a group of node(s) can be extracted from the movement records of nodes. Then the set of commonly visited locations for the relay node(s) set and the destination node is obtained, by which we can further evaluate the co-delivery probability of the relay node(s) set. Both local search scheme and tabu-search scheme are utilized in finding the optimal set, and the tabu-search based routing Tabu-MPAR is proved able to guide the relay node(s) set in evolving to the optimal one. We demonstrate how the MPAR algorithm significantly outperforms the previous ones through extensive simulations, based on the synthetic SDTN mobility model.Comment: 18 page

Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support

In a variety of research areas, the weighted bag of vectors and the histogram are widely used descriptors for complex objects. Both can be expressed as discrete distributions. D2-clustering pursues the minimum total within-cluster variation for a set of discrete distributions subject to the Kantorovich-Wasserstein metric. D2-clustering has a severe scalability issue, the bottleneck being the computation of a centroid distribution, called Wasserstein barycenter, that minimizes its sum of squared distances to the cluster members. In this paper, we develop a modified Bregman ADMM approach for computing the approximate discrete Wasserstein barycenter of large clusters. In the case when the support points of the barycenters are unknown and have low cardinality, our method achieves high accuracy empirically at a much reduced computational cost. The strengths and weaknesses of our method and its alternatives are examined through experiments, and we recommend scenarios for their respective usage. Moreover, we develop both serial and parallelized versions of the algorithm. By experimenting with large-scale data, we demonstrate the computational efficiency of the new methods and investigate their convergence properties and numerical stability. The clustering results obtained on several datasets in different domains are highly competitive in comparison with some widely used methods in the corresponding areas.Comment: double-column, 17 pages, 3 figures, 5 tables. English usage improve

RES-PCA: A Scalable Approach to Recovering Low-rank Matrices

Robust principal component analysis (RPCA) has drawn significant attentions due to its powerful capability in recovering low-rank matrices as well as successful appplications in various real world problems. The current state-of-the-art algorithms usually need to solve singular value decomposition of large matrices, which generally has at least a quadratic or even cubic complexity. This drawback has limited the application of RPCA in solving real world problems. To combat this drawback, in this paper we propose a new type of RPCA method, RES-PCA, which is linearly efficient and scalable in both data size and dimension. For comparison purpose, AltProj, an existing scalable approach to RPCA requires the precise knowlwdge of the true rank; otherwise, it may fail to recover low-rank matrices. By contrast, our method works with or without knowing the true rank; even when both methods work, our method is faster. Extensive experiments have been performed and testified to the effectiveness of proposed method quantitatively and in visual quality, which suggests that our method is suitable to be employed as a light-weight, scalable component for RPCA in any application pipelines

Convolutional Geometric Matrix Completion

Geometric matrix completion (GMC) has been proposed for recommendation by integrating the relationship (link) graphs among users/items into matrix completion (MC). Traditional GMC methods typically adopt graph regularization to impose smoothness priors for MC. Recently, geometric deep learning on graphs (GDLG) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods. To the best of our knowledge, there exists only one GDLG method for GMC, which is called RMGCNN. RMGCNN combines graph convolutional network (GCN) and recurrent neural network (RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method. In this paper, we propose a new GMC method, called convolutional geometric matrix completion (CGMC), for recommendation with graphs among users/items. CGMC is a pure GCN-based method with a newly designed graph convolutional network. Experimental results on real datasets show that CGMC can outperform other state-of-the-art methods including RMGCNN in terms of both accuracy and speed