9,577 research outputs found
Information Cascades on Arbitrary Topologies
In this paper, we study information cascades on graphs. In this setting, each
node in the graph represents a person. One after another, each person has to
take a decision based on a private signal as well as the decisions made by
earlier neighboring nodes. Such information cascades commonly occur in practice
and have been studied in complete graphs where everyone can overhear the
decisions of every other player. It is known that information cascades can be
fragile and based on very little information, and that they have a high
likelihood of being wrong.
Generalizing the problem to arbitrary graphs reveals interesting insights. In
particular, we show that in a random graph , for the right value of
, the number of nodes making a wrong decision is logarithmic in . That
is, in the limit for large , the fraction of players that make a wrong
decision tends to zero. This is intriguing because it contrasts to the two
natural corner cases: empty graph (everyone decides independently based on his
private signal) and complete graph (all decisions are heard by all nodes). In
both of these cases a constant fraction of nodes make a wrong decision in
expectation. Thus, our result shows that while both too little and too much
information sharing causes nodes to take wrong decisions, for exactly the right
amount of information sharing, asymptotically everyone can be right. We further
show that this result in random graphs is asymptotically optimal for any
topology, even if nodes follow a globally optimal algorithmic strategy. Based
on the analysis of random graphs, we explore how topology impacts global
performance and construct an optimal deterministic topology among layer graphs
Multi-View 3D Object Detection Network for Autonomous Driving
This paper aims at high-accuracy 3D object detection in autonomous driving
scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework
that takes both LIDAR point cloud and RGB images as input and predicts oriented
3D bounding boxes. We encode the sparse 3D point cloud with a compact
multi-view representation. The network is composed of two subnetworks: one for
3D object proposal generation and another for multi-view feature fusion. The
proposal network generates 3D candidate boxes efficiently from the bird's eye
view representation of 3D point cloud. We design a deep fusion scheme to
combine region-wise features from multiple views and enable interactions
between intermediate layers of different paths. Experiments on the challenging
KITTI benchmark show that our approach outperforms the state-of-the-art by
around 25% and 30% AP on the tasks of 3D localization and 3D detection. In
addition, for 2D detection, our approach obtains 10.3% higher AP than the
state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Searching for Charged Higgs Boson in Polarized Top Quark
The charged Higgs boson is quite common in many new physics models. In this
study we examine the potential of observing a heavy charged Higgs boson in its
decay mode of top-quark and bottom-quark in the Type-II
Two-Higgs-Doublet-Model. In this model, the chirality structure of the coupling
of charged Higgs boson to the top- and bottom-quark is very sensitive to the
value of . As the polarization of the top-quark can be measured
experimentally from the top-quark decay products, one could make use of the
top-quark polarization to determine the value of . We preform a
detailed analysis of measuring top-quark polarization in the production
channels and . We calculate the helicity
amplitudes of the charged Higgs boson production and decay.Our calculation
shows that the top-quark from the charged Higgs boson decay provides a good
probe for measuring , especially for the intermediate
region. On the contrary, the top-quark produced in association with the charged
Higgs boson cannot be used to measure because its polarization is
highly contaminated by the -channel kinematics.Comment: 21 pages, 12 figures, 2 table
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