1,745 research outputs found
Open manifolds with asymptotically nonnegative Ricci curvature and large volume growth
In this paper, we study the topology of complete noncompact Riemannian
manifolds with asymptotically nonnegative Ricci curvature and large volume
growth. We prove that they have finite topological types under some curvature
decay and volume growth conditions. We also generize it to the manifolds with
-th asymptotically nonnegative Ricci curvature by using extensions of
Abresch-Gromoll's excess function estimate.Comment: 11 page
On the volume of sectional-hyperbolic sets
For a transitive sectional-hypebolic set with positive volume on a
-dimensional manifold (), we show that and
is a uniformly hyperbolic set without singularitiesComment: 6 page
An alternative proof of lower bounds for the first eigenvalue on manifolds
Recently, Andrews and Clutterbuck [AC13] gave a new proof of the optimal
lower eigenvalue bound on manifolds via modulus of continuity for solutions of
the heat equation. In this short note, we give an alternative proof of Theorem
2 in [AC13]. More precisely, following Ni's method ([Ni13, Section 6]) we give
an elliptic proof of this theorem.Comment: All comments are welcom
Paper evolution graph: Multi-view structural retrieval for academic literature
Academic literature retrieval is concerned with the selection of papers that
are most likely to match a user's information needs. Most of the retrieval
systems are limited to list-output models, in which the retrieval results are
isolated from each other. In this work, we aim to uncover the relationships of
the retrieval results and propose a method for building structural retrieval
results for academic literatures, which we call a paper evolution graph (PEG).
A PEG describes the evolution of the diverse aspects of input queries through
several evolution chains of papers. By utilizing the author, citation and
content information, PEGs can uncover the various underlying relationships
among the papers and present the evolution of articles from multiple
viewpoints. Our system supports three types of input queries: keyword,
single-paper and two-paper queries. The construction of a PEG mainly consists
of three steps. First, the papers are soft-clustered into communities via
metagraph factorization during which the topic distribution of each paper is
obtained. Second, topically cohesive evolution chains are extracted from the
communities that are relevant to the query. Each chain focuses on one aspect of
the query. Finally, the extracted chains are combined to generate a PEG, which
fully covers all the topics of the query. The experimental results on a
real-world dataset demonstrate that the proposed method is able to construct
meaningful PEGs
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
We have witnessed rapid evolution of deep neural network architecture design
in the past years. These latest progresses greatly facilitate the developments
in various areas such as computer vision and natural language processing.
However, along with the extraordinary performance, these state-of-the-art
models also bring in expensive computational cost. Directly deploying these
models into applications with real-time requirement is still infeasible.
Recently, Hinton etal. have shown that the dark knowledge within a powerful
teacher model can significantly help the training of a smaller and faster
student network. These knowledge are vastly beneficial to improve the
generalization ability of the student model. Inspired by their work, we
introduce a new type of knowledge -- cross sample similarities for model
compression and acceleration. This knowledge can be naturally derived from deep
metric learning model. To transfer them, we bring the "learning to rank"
technique into deep metric learning formulation. We test our proposed DarkRank
method on various metric learning tasks including pedestrian re-identification,
image retrieval and image clustering. The results are quite encouraging. Our
method can improve over the baseline method by a large margin. Moreover, it is
fully compatible with other existing methods. When combined, the performance
can be further boosted
A Deep Optimization Approach for Image Deconvolution
In blind image deconvolution, priors are often leveraged to constrain the
solution space, so as to alleviate the under-determinacy. Priors which are
trained separately from the task of deconvolution tend to be instable, or
ineffective. We propose the Golf Optimizer, a novel but simple form of network
that learns deep priors from data with better propagation behavior. Like
playing golf, our method first estimates an aggressive propagation towards
optimum using one network, and recurrently applies a residual CNN to learn the
gradient of prior for delicate correction on restoration. Experiments show that
our network achieves competitive performance on GoPro dataset, and our model is
extremely lightweight compared with the state-of-art works.Comment: 12 pages, 16 figure
Visualization of Hyperspectral Images Using Moving Least Squares
Displaying the large number of bands in a hyper spectral image on a
trichromatic monitor has been an active research topic. The visualized image
shall convey as much information as possible form the original data and
facilitate image interpretation. Most existing methods display HSIs in false
colors which contradict with human's experience and expectation. In this paper,
we propose a nonlinear approach to visualize an input HSI with natural colors
by taking advantage of a corresponding RGB image. Our approach is based on
Moving Least Squares, an interpolation scheme for reconstructing a surface from
a set of control points, which in our case is a set of matching pixels between
the HSI and the corresponding RGB image. Based on MLS, the proposed method
solves for each spectral signature a unique transformation so that the non
linear structure of the HSI can be preserved. The matching pixels between a
pair of HSI and RGB image can be reused to display other HSIs captured b the
same imaging sensor with natural colors. Experiments show that the output image
of the proposed method no only have natural colors but also maintain the visual
information necessary for human analysis.Comment: arXiv admin note: text overlap with arXiv:1712.0165
Neural Trojans
While neural networks demonstrate stronger capabilities in pattern
recognition nowadays, they are also becoming larger and deeper. As a result,
the effort needed to train a network also increases dramatically. In many
cases, it is more practical to use a neural network intellectual property (IP)
that an IP vendor has already trained. As we do not know about the training
process, there can be security threats in the neural IP: the IP vendor
(attacker) may embed hidden malicious functionality, i.e. neural Trojans, into
the neural IP. We show that this is an effective attack and provide three
mitigation techniques: input anomaly detection, re-training, and input
preprocessing. All the techniques are proven effective. The input anomaly
detection approach is able to detect 99.8% of Trojan triggers although with
12.2% false positive. The re-training approach is able to prevent 94.1% of
Trojan triggers from triggering the Trojan although it requires that the neural
IP be reconfigurable. In the input preprocessing approach, 90.2% of Trojan
triggers are rendered ineffective and no assumption about the neural IP is
needed.Comment: The shorth-length version of this paper is to appear in the 2017 IEEE
International Conference on Computer Design (ICCD
Positive Geometries and Canonical Forms
Recent years have seen a surprising connection between the physics of
scattering amplitudes and a class of mathematical objects--the positive
Grassmannian, positive loop Grassmannians, tree and loop Amplituhedra--which
have been loosely referred to as "positive geometries". The connection between
the geometry and physics is provided by a unique differential form canonically
determined by the property of having logarithmic singularities (only) on all
the boundaries of the space, with residues on each boundary given by the
canonical form on that boundary. In this paper we initiate an exploration of
"positive geometries" and "canonical forms" as objects of study in their own
right in a more general mathematical setting. We give a precise definition of
positive geometries and canonical forms, introduce general methods for finding
forms for more complicated positive geometries from simpler ones, and present
numerous examples of positive geometries in projective spaces, Grassmannians,
and toric, cluster and flag varieties. We also illustrate a number of
strategies for computing canonical forms which yield interesting
representations for the forms associated with wide classes of positive
geometries, ranging from the simplest Amplituhedra to new expressions for the
volume of arbitrary convex polytopes.Comment: 123 pages, 12 figures, v2: fixed a reference and some minor typo
Constrained Manifold Learning for Hyperspectral Imagery Visualization
Displaying the large number of bands in a hyper- spectral image (HSI) on a
trichromatic monitor is important for HSI processing and analysis system. The
visualized image shall convey as much information as possible from the original
HSI and meanwhile facilitate image interpretation. However, most existing
methods display HSIs in false color, which contradicts with user experience and
expectation. In this paper, we propose a visualization approach based on
constrained manifold learning, whose goal is to learn a visualized image that
not only preserves the manifold structure of the HSI but also has natural
colors. Manifold learning preserves the image structure by forcing pixels with
similar signatures to be displayed with similar colors. A composite kernel is
applied in manifold learning to incorporate both the spatial and spectral
information of HSI in the embedded space. The colors of the output image are
constrained by a corresponding natural-looking RGB image, which can either be
generated from the HSI itself (e.g., band selection from the visible
wavelength) or be captured by a separate device. Our method can be done at
instance-level and feature-level. Instance-level learning directly obtains the
RGB coordinates for the pixels in the HSI while feature-level learning learns
an explicit mapping function from the high dimensional spectral space to the
RGB space. Experimental results demonstrate the advantage of the proposed
method in information preservation and natural color visualization
- …