763 research outputs found
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Model study of soil-moisture influence on precipitation seesaw in the southern United States
Nonparametric Clustering of Mixed Data Using Modified Chi-square Tests
We propose a non-parametric method to cluster mixed data containing both
continuous and discrete random variables. The product space of continuous and
categorical sample spaces is approximated locally by analyzing neighborhoods
with cluster patterns. Detection of cluster patterns on the product space is
determined by using a modified Chi-square test. The proposed method does not
impose a global distance function which could be difficult to specify in
practice. Results from simulation studies have shown that our proposed methods
out-performed the benchmark method, AutoClass, for various settings
Basic Properties of Singular Fractional Order System with order (1,2)
This paper focuses on some properties, which include regularity, impulse,
stability, admissibility and robust admissibility, of singular fractional order
system (SFOS) with fractional order . The finitions of regularity,
impulse-free, stability and admissibility are given in the paper. Regularity is
analysed in time domain and the analysis of impulse-free is based on state
response. A sufficient and necessary condition of stability is established.
Three different sufficient and necessary conditions of admissibility are
proved. Then, this paper shows how to get the numerical solution of SFOS in
time domain. Finally, a numerical example is provided to illustrate the
proposed conditions.Comment: 28 pages, 2 figures, journa
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
Depth estimation and scene parsing are two particularly important tasks in
visual scene understanding. In this paper we tackle the problem of simultaneous
depth estimation and scene parsing in a joint CNN. The task can be typically
treated as a deep multi-task learning problem [42]. Different from previous
methods directly optimizing multiple tasks given the input training data, this
paper proposes a novel multi-task guided prediction-and-distillation network
(PAD-Net), which first predicts a set of intermediate auxiliary tasks ranging
from low level to high level, and then the predictions from these intermediate
auxiliary tasks are utilized as multi-modal input via our proposed multi-modal
distillation modules for the final tasks. During the joint learning, the
intermediate tasks not only act as supervision for learning more robust deep
representations but also provide rich multi-modal information for improving the
final tasks. Extensive experiments are conducted on two challenging datasets
(i.e. NYUD-v2 and Cityscapes) for both the depth estimation and scene parsing
tasks, demonstrating the effectiveness of the proposed approach.Comment: Accepted at CVPR 201
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
This paper presents a novel method for detecting pedestrians under adverse
illumination conditions. Our approach relies on a novel cross-modality learning
framework and it is based on two main phases. First, given a multimodal
dataset, a deep convolutional network is employed to learn a non-linear
mapping, modeling the relations between RGB and thermal data. Then, the learned
feature representations are transferred to a second deep network, which
receives as input an RGB image and outputs the detection results. In this way,
features which are both discriminative and robust to bad illumination
conditions are learned. Importantly, at test time, only the second pipeline is
considered and no thermal data are required. Our extensive evaluation
demonstrates that the proposed approach outperforms the state-of- the-art on
the challenging KAIST multispectral pedestrian dataset and it is competitive
with previous methods on the popular Caltech dataset.Comment: Accepted at CVPR 201
Monocular Depth Estimation using Multi-Scale Continuous CRFs as Sequential Deep Networks
Depth cues have been proved very useful in various computer vision and
robotic tasks. This paper addresses the problem of monocular depth estimation
from a single still image. Inspired by the effectiveness of recent works on
multi-scale convolutional neural networks (CNN), we propose a deep model which
fuses complementary information derived from multiple CNN side outputs.
Different from previous methods using concatenation or weighted average
schemes, the integration is obtained by means of continuous Conditional Random
Fields (CRFs). In particular, we propose two different variations, one based on
a cascade of multiple CRFs, the other on a unified graphical model. By
designing a novel CNN implementation of mean-field updates for continuous CRFs,
we show that both proposed models can be regarded as sequential deep networks
and that training can be performed end-to-end. Through an extensive
experimental evaluation, we demonstrate the effectiveness of the proposed
approach and establish new state of the art results for the monocular depth
estimation task on three publicly available datasets, i.e. NYUD-V2, Make3D and
KITTI.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0215
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation
This paper addresses the problem of depth estimation from a single still
image. Inspired by recent works on multi- scale convolutional neural networks
(CNN), we propose a deep model which fuses complementary information derived
from multiple CNN side outputs. Different from previous methods, the
integration is obtained by means of continuous Conditional Random Fields
(CRFs). In particular, we propose two different variations, one based on a
cascade of multiple CRFs, the other on a unified graphical model. By designing
a novel CNN implementation of mean-field updates for continuous CRFs, we show
that both proposed models can be regarded as sequential deep networks and that
training can be performed end-to-end. Through extensive experimental evaluation
we demonstrate the effective- ness of the proposed approach and establish new
state of the art results on publicly available datasets.Comment: Accepted as a spotlight paper at CVPR 201
Learning to Group and Label Fine-Grained Shape Components
A majority of stock 3D models in modern shape repositories are assembled with
many fine-grained components. The main cause of such data form is the
component-wise modeling process widely practiced by human modelers. These
modeling components thus inherently reflect some function-based shape
decomposition the artist had in mind during modeling. On the other hand,
modeling components represent an over-segmentation since a functional part is
usually modeled as a multi-component assembly. Based on these observations, we
advocate that labeled segmentation of stock 3D models should not overlook the
modeling components and propose a learning solution to grouping and labeling of
the fine-grained components. However, directly characterizing the shape of
individual components for the purpose of labeling is unreliable, since they can
be arbitrarily tiny and semantically meaningless. We propose to generate part
hypotheses from the components based on a hierarchical grouping strategy, and
perform labeling on those part groups instead of directly on the components.
Part hypotheses are mid-level elements which are more probable to carry
semantic information. A multiscale 3D convolutional neural network is trained
to extract context-aware features for the hypotheses. To accomplish a labeled
segmentation of the whole shape, we formulate higher-order conditional random
fields (CRFs) to infer an optimal label assignment for all components.
Extensive experiments demonstrate that our method achieves significantly robust
labeling results on raw 3D models from public shape repositories. Our work also
contributes the first benchmark for component-wise labeling.Comment: Accepted to SIGGRAPH Asia 2018. Corresponding Author: Kai Xu
([email protected]
Edge-Preserving Piecewise Linear Image Smoothing Using Piecewise Constant Filters
Most image smoothing filters in the literature assume a piecewise constant
model of smoothed output images. However, the piecewise constant model
assumption can cause artifacts such as gradient reversals in applications such
as image detail enhancement, HDR tone mapping, etc. In these applications, a
piecewise linear model assumption is more preferred. In this paper, we propose
a simple yet very effective framework to smooth images of piecewise linear
model assumption using classical filters with the piecewise constant model
assumption. Our method is capable of handling with gradient reversal artifacts
caused by the piecewise constant model assumption. In addition, our method can
further help accelerated methods, which need to quantize image intensity values
into different bins, to achieve similar results that need a large number of
bins using a much smaller number of bins. This can greatly reduce the
computational cost. We apply our method to various classical filters with the
piecewise constant model assumption. Experimental results of several
applications show the effectiveness of the proposed method
A new test of gravity with the cosmological standard rulers in radio quasars
As an important candidate gravity theory alternative to dark energy, a class
of modified gravity, which introduces a perturbation of the Ricci scalar
in the Einstein-Hilbert action, has been extensively applied to cosmology
to explain the acceleration of the universe. In this paper, we focus on the
recently-released VLBI observations of the compact structure in
intermediate-luminosity quasars combined with the angular-diameter-distance
measurements from galaxy clusters, which consists of 145 data points performing
as individual cosmological standard rulers in the redshift range , to investigate observational constraints on two viable models in
theories within the Palatini formalism: and
. We also combine the individual standard ruler data
with the observations of CMB and BAO, which provides stringent constraints.
Furthermore, two model diagnostics, and statefinder, are also applied
to distinguish the two models and CDM model. Our results show
that (1) The quasars sample performs very well to place constraints on the two
cosmologies, which indicates its potential to act as a powerful
complementary probe to other cosmological standard rulers. (2) The CDM
model, which corresponds to in the two cosmologies is still
included within range. However, there still exists some possibility
that CDM may not the best cosmological model preferred by the current
high-redshift observations. (3) The information criteria indicate that the
cosmological constant model is still the best one, while the model
gets the smallest observational support. (4) The model, which evolves
quite different from model at early times, still significantly
deviates from both and CDM model at the present time.Comment: 18 pages, 5 figures, accepted for publication in JCA
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