10,584 research outputs found
On ends of finite-volume noncompact mainfolds of nonpositive curvature
In this paper we confirm a folklore conjecture which suggests that for a
complete noncompact manifold of finite volume with sectional curvature , if the universal cover of is a visibility manifold, then
the fundamental group of each end of is almost nilpotent. Applications on
the geometry and topology of noncompact nonpositively curved manifolds will be
discussed.Comment: 35 pages, 2 figures. Comments are welcom
Log-Sobolev, isoperimetry and transport inequalities on graphs
In this paper, we study some functional inequalities (such as Poincar\'e
inequalities, logarithmic Sobolev inequalities, generalized Cheeger
isoperimetric inequalities, transportation-information inequalities and
transportation-entropy inequalities) for reversible nearest-neighbor Markov
processes on a connected finite graph by means of (random) path method. We
provide estimates of the involved constants
Differential Variable Speed Limits Control for Freeway Recurrent Bottlenecks via Deep Reinforcement learning
Variable speed limits (VSL) control is a flexible way to improve traffic
condition,increase safety and reduce emission. There is an emerging trend of
using reinforcement learning technique for VSL control and recent studies have
shown promising results. Currently, deep learning is enabling reinforcement
learning to develope autonomous control agents for problems that were
previously intractable. In this paper, we propose a more effective deep
reinforcement learning (DRL) model for differential variable speed limits
(DVSL) control, in which the dynamic and different speed limits among lanes can
be imposed. The proposed DRL models use a novel actor-critic architecture which
can learn a large number of discrete speed limits in a continues action space.
Different reward signals, e.g. total travel time, bottleneck speed, emergency
braking, and vehicular emission are used to train the DVSL controller, and
comparison between these reward signals are conducted. We test proposed DRL
baased DVSL controllers on a simulated freeway recurrent bottleneck. Results
show that the efficiency, safety and emissions can be improved by the proposed
method. We also show some interesting findings through the visulization of the
control policies generated from DRL models.Comment: 24 pages, 7 figures, 1 tabl
Morse Index Theorem of Lagrangian Systems and Stability of Brake Orbit
In this paper, we prove Morse index theorem of Lagrangian system with any
self-adjoint boundary conditions. Based on it, we give some nontrivial
estimation on the difference of Morse indices. As an application, we get a new
criterion for the stability problem of brake periodic orbit.Comment: 24 page
Adversarial Discriminative Heterogeneous Face Recognition
The gap between sensing patterns of different face modalities remains a
challenging problem in heterogeneous face recognition (HFR). This paper
proposes an adversarial discriminative feature learning framework to close the
sensing gap via adversarial learning on both raw-pixel space and compact
feature space. This framework integrates cross-spectral face hallucination and
discriminative feature learning into an end-to-end adversarial network. In the
pixel space, we make use of generative adversarial networks to perform
cross-spectral face hallucination. An elaborate two-path model is introduced to
alleviate the lack of paired images, which gives consideration to both global
structures and local textures. In the feature space, an adversarial loss and a
high-order variance discrepancy loss are employed to measure the global and
local discrepancy between two heterogeneous distributions respectively. These
two losses enhance domain-invariant feature learning and modality independent
noise removing. Experimental results on three NIR-VIS databases show that our
proposed approach outperforms state-of-the-art HFR methods, without requiring
of complex network or large-scale training dataset
Scalar waves from a star orbiting a BTZ black hole
In this paper we compute the decay rates of massless scalar waves excited by
a star circularly orbiting around the non-extremal (general) and extremal BTZ
black holes. These decay rates are compared with the corresponding quantities
computed in the corresponding dual conformal field theories respectively. We
find that matches are achieved in both cases.Comment: In v2, 17 pages, title changed (contents not changed), discussion of
the isometry group of the near-horizon-extremal BTZ geometry and its effects
on the solutions is added, references added. V3, minor corrections, several
more references adde
Coupled Deep Learning for Heterogeneous Face Recognition
Heterogeneous face matching is a challenge issue in face recognition due to
large domain difference as well as insufficient pairwise images in different
modalities during training. This paper proposes a coupled deep learning (CDL)
approach for the heterogeneous face matching. CDL seeks a shared feature space
in which the heterogeneous face matching problem can be approximately treated
as a homogeneous face matching problem. The objective function of CDL mainly
includes two parts. The first part contains a trace norm and a block-diagonal
prior as relevance constraints, which not only make unpaired images from
multiple modalities be clustered and correlated, but also regularize the
parameters to alleviate overfitting. An approximate variational formulation is
introduced to deal with the difficulties of optimizing low-rank constraint
directly. The second part contains a cross modal ranking among triplet domain
specific images to maximize the margin for different identities and increase
data for a small amount of training samples. Besides, an alternating
minimization method is employed to iteratively update the parameters of CDL.
Experimental results show that CDL achieves better performance on the
challenging CASIA NIR-VIS 2.0 face recognition database, the IIIT-D Sketch
database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF),
which significantly outperforms state-of-the-art heterogeneous face recognition
methods.Comment: AAAI 201
Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition
Heterogeneous face recognition (HFR) aims to match facial images acquired
from different sensing modalities with mission-critical applications in
forensics, security and commercial sectors. However, HFR is a much more
challenging problem than traditional face recognition because of large
intra-class variations of heterogeneous face images and limited training
samples of cross-modality face image pairs. This paper proposes a novel
approach namely Wasserstein CNN (convolutional neural networks, or WCNN for
short) to learn invariant features between near-infrared and visual face images
(i.e. NIR-VIS face recognition). The low-level layers of WCNN are trained with
widely available face images in visual spectrum. The high-level layer is
divided into three parts, i.e., NIR layer, VIS layer and NIR-VIS shared layer.
The first two layers aims to learn modality-specific features and NIR-VIS
shared layer is designed to learn modality-invariant feature subspace.
Wasserstein distance is introduced into NIR-VIS shared layer to measure the
dissimilarity between heterogeneous feature distributions. So W-CNN learning
aims to achieve the minimization of Wasserstein distance between NIR
distribution and VIS distribution for invariant deep feature representation of
heterogeneous face images. To avoid the over-fitting problem on small-scale
heterogeneous face data, a correlation prior is introduced on the
fully-connected layers of WCNN network to reduce parameter space. This prior is
implemented by a low-rank constraint in an end-to-end network. The joint
formulation leads to an alternating minimization for deep feature
representation at training stage and an efficient computation for heterogeneous
data at testing stage. Extensive experiments on three challenging NIR-VIS face
recognition databases demonstrate the significant superiority of Wasserstein
CNN over state-of-the-art methods
A Light CNN for Deep Face Representation with Noisy Labels
The volume of convolutional neural network (CNN) models proposed for face
recognition has been continuously growing larger to better fit large amount of
training data. When training data are obtained from internet, the labels are
likely to be ambiguous and inaccurate. This paper presents a Light CNN
framework to learn a compact embedding on the large-scale face data with
massive noisy labels. First, we introduce a variation of maxout activation,
called Max-Feature-Map (MFM), into each convolutional layer of CNN. Different
from maxout activation that uses many feature maps to linearly approximate an
arbitrary convex activation function, MFM does so via a competitive
relationship. MFM can not only separate noisy and informative signals but also
play the role of feature selection between two feature maps. Second, three
networks are carefully designed to obtain better performance meanwhile reducing
the number of parameters and computational costs. Lastly, a semantic
bootstrapping method is proposed to make the prediction of the networks more
consistent with noisy labels. Experimental results show that the proposed
framework can utilize large-scale noisy data to learn a Light model that is
efficient in computational costs and storage spaces. The learned single network
with a 256-D representation achieves state-of-the-art results on various face
benchmarks without fine-tuning. The code is released on
https://github.com/AlfredXiangWu/LightCNN.Comment: arXiv admin note: text overlap with arXiv:1507.04844. The models are
released on https://github.com/AlfredXiangWu/LightCNN, IEEE Transactions on
Information Forensics and Security, 201
ES-CTC: A Deep Neuroevolution Model for Cooperative Intelligent Freeway Traffic Control
Cooperative intelligent freeway traffic control is an important application
in intelligent transportation systems, which is expected to improve the
mobility of freeway networks. In this paper, we propose a deep neuroevolution
model, called ES-CTC, to achieve a cooperative control scheme of ramp metering,
differential variable speed limits and lane change control agents for improving
freeway traffic. In this model, the graph convolutional networks are used to
learn more meaningful spatial pattern from traffic sensors, a knowledge sharing
layer is designed for communication between different agents. The proposed
neural networks structure allows different agents share knowledge with each
other and execute action asynchronously. In order to address the delayed reward
and action asynchronism issues, the evolutionary strategy is utilized to train
the agents under stochastic traffic demands. The experimental results on a
simulated freeway section indicate that ES-CTC is a viable approach and
outperforms several existing methodsComment: 7 page
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