169 research outputs found
Dynamics of social contagions with local trend imitation
Research on social contagion dynamics has not yet including a theoretical
analysis of the ubiquitous local trend imitation (LTI) characteristic. We
propose a social contagion model with a tent-like adoption probability
distribution to investigate the effect of this LTI characteristic on behavior
spreading. We also propose a generalized edge-based compartmental theory to
describe the proposed model. Through extensive numerical simulations and
theoretical analyses, we find a crossover in the phase transition: when the LTI
capacity is strong, the growth of the final behavior adoption size exhibits a
second-order phase transition. When the LTI capacity is weak, we see a
first-order phase transition. For a given behavioral information transmission
probability, there is an optimal LTI capacity that maximizes the final behavior
adoption size. Finally we find that the above phenomena are not qualitatively
affected by the heterogeneous degree distribution. Our suggested theory agrees
with the simulation results.Comment: 14 pages, 5 figure
A Single Multi-Task Deep Neural Network with a Multi-Scale Feature Aggregation Mechanism for Manipulation Relationship Reasoning in Robotic Grasping
Grasping specific objects in complex and irregularly stacked scenes is still
challenging for robotics. Because the robot is not only required to identify
the object's grasping posture but also needs to reason the manipulation
relationship between the objects. In this paper, we propose a manipulation
relationship reasoning network with a multi-scale feature aggregation (MSFA)
mechanism for robot grasping tasks. MSFA aggregates high-level semantic
information and low-level spatial information in a cross-scale connection way
to improve the generalization ability of the model. Furthermore, to improve the
accuracy, we propose to use intersection features with rich location priors for
manipulation relationship reasoning. Experiments are validated in VMRD datasets
and real environments, respectively. The experimental results demonstrate that
our proposed method can accurately predict the manipulation relationship
between objects in the scene of multi-object stacking. Compared with previous
methods, it significantly improves reasoning speed and accuracy
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