100 research outputs found
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Unlike on images, semantic learning on 3D point clouds using a deep network
is challenging due to the naturally unordered data structure. Among existing
works, PointNet has achieved promising results by directly learning on point
sets. However, it does not take full advantage of a point's local neighborhood
that contains fine-grained structural information which turns out to be helpful
towards better semantic learning. In this regard, we present two new operations
to improve PointNet with a more efficient exploitation of local structures. The
first one focuses on local 3D geometric structures. In analogy to a convolution
kernel for images, we define a point-set kernel as a set of learnable 3D points
that jointly respond to a set of neighboring data points according to their
geometric affinities measured by kernel correlation, adapted from a similar
technique for point cloud registration. The second one exploits local
high-dimensional feature structures by recursive feature aggregation on a
nearest-neighbor-graph computed from 3D positions. Experiments show that our
network can efficiently capture local information and robustly achieve better
performances on major datasets. Our code is available at
http://www.merl.com/research/license#KCNetComment: Accepted in CVPR'18. *indicates equal contributio
Distributed Flow Scheduling in an Unknown Environment
Flow scheduling tends to be one of the oldest and most stubborn problems in
networking. It becomes more crucial in the next generation network, due to fast
changing link states and tremendous cost to explore the global structure. In
such situation, distributed algorithms often dominate. In this paper, we design
a distributed virtual game to solve the flow scheduling problem and then
generalize it to situations of unknown environment, where online learning
schemes are utilized. In the virtual game, we use incentives to stimulate
selfish users to reach a Nash Equilibrium Point which is valid based on the
analysis of the `Price of Anarchy'. In the unknown-environment generalization,
our ultimate goal is the minimization of cost in the long run. In order to
achieve balance between exploration of routing cost and exploitation based on
limited information, we model this problem based on Multi-armed Bandit Scenario
and combined newly proposed DSEE with the virtual game design. Armed with these
powerful tools, we find a totally distributed algorithm to ensure the
logarithmic growing of regret with time, which is optimum in classic
Multi-armed Bandit Problem. Theoretical proof and simulation results both
affirm this claim. To our knowledge, this is the first research to combine
multi-armed bandit with distributed flow scheduling.Comment: 10 pages, 3 figures, conferenc
An Efficient Water-Filling Algorithm for Power Allocation in OFDM-Based Cognitive Radio Systems
In this paper, we present a new water-filling algorithm for power allocation in Orthogonal Frequency Division Multiplexing (OFDM) – based cognitive radio systems. The conventional water-filling algorithm cannot be directly employed for power allocation in a cognitive radio system, because there are more power constraints in the cognitive radio power allocation problem than in the classic OFDM system. In this paper, a novel algorithm based on iterative water-filling is presented to overcome such limitations. However, the computational complexity in iterative water-filling is very high. Thus, we explore features of the water-filling algorithm and propose a low-complexity algorithm using power-increment or power-decrement water-filling processes. Simulation results show that our proposed algorithms can achieve the optimal power allocation performance in less time than the iterative water-filling algorithms
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