539 research outputs found
Joint Community Detection and Rotational Synchronization via Semidefinite Programming
In the presence of heterogeneous data, where randomly rotated objects fall
into multiple underlying categories, it is challenging to simultaneously
classify them into clusters and synchronize them based on pairwise relations.
This gives rise to the joint problem of community detection and
synchronization. We propose a series of semidefinite relaxations, and prove
their exact recovery when extending the celebrated stochastic block model to
this new setting where both rotations and cluster identities are to be
determined. Numerical experiments demonstrate the efficacy of our proposed
algorithms and confirm our theoretical result which indicates a sharp phase
transition for exact recovery
Bio-electrolytic sensor for rapid monitoring of volatile fatty acids in anaerobic digestion process
Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
This paper aims to tackle the interactive behavior prediction task, and
proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP)
framework to jointly generate scene-compliant trajectories of two interacting
agents. Our CGTP framework is an end to end and interpretable model, including
three main stages: context encoding, goal interactive prediction and trajectory
interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed
to extract the interactive features between agent-to-agent and agent-to-goals
using a graph-based vectorized representation. Further, the Conditional Goal
Prediction Network (CGPNet) focuses on goal interactive prediction via a
combined form of marginal and conditional goal predictors. Finally, the
Goaloriented Trajectory Forecasting Network (GTFNet) is proposed to implement
trajectory interactive prediction via the conditional goal-oriented predictors,
with the predicted future states of the other interacting agent taken as
inputs. In addition, a new goal interactive loss is developed to better learn
the joint probability distribution over goal candidates between two interacting
agents. In the end, the proposed method is conducted on Argoverse motion
forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset.
The comparative results demonstrate the superior performance of our proposed
CGTP model than the mainstream prediction methods.Comment: 14 pages, 4 figure
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