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 $G(n,q)$, for the right value of $q$, the number of nodes making a wrong decision is logarithmic in $n$. That is, in the limit for large $n$, 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 $\tan\beta$. 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 $\tan\beta$. We preform a detailed analysis of measuring top-quark polarization in the production channels $gb\to tH^-$ and $g\bar{b}\to \bar{t}H^+$. 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 $\tan\beta$, especially for the intermediate $\tan\beta$ region. On the contrary, the top-quark produced in association with the charged Higgs boson cannot be used to measure $\tan\beta$ because its polarization is highly contaminated by the $t$-channel kinematics.Comment: 21 pages, 12 figures, 2 table