2,222 research outputs found
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
( being the interface normal and
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
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, -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 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 , called mapper. maps both real data
distribution and generated data distribution from the original data space to a
feature representation space , and it is trained to maximize MMD
between the two mapped distributions in , while the generator
tries to minimize the MMD. We call the new model generative adversarial mapping
networks (GAMNs). We demonstrate that the adversarial mapper can help
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
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
(), constant jerk () and third order
expansion of luminosity distance () 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. and
give value of transition redshift about . gives a
larger one, say . The implies almost the same goodness
of the models. But, for its badness of evolution of deceleration parameter at
high redshift , can not be reliable. and are compatible
with CDM model at the and confidence levels
respectively. is not compatible with CDM model at
confidence level. From and models, one can conclude that the cosmic
data favor a cosmological model having .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
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
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
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