16,369 research outputs found
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly
challenging problem, because the demand is simultaneously influenced by the
complex interactions among many spatial and temporal factors and other external
factors such as weather. To address this problem, we propose a Spatio-TEmporal
Fuzzy neural Network (STEF-Net) to accurately predict passenger demands
incorporating the complex interactions of all known important factors. We
design an end-to-end learning framework with different neural networks modeling
different factors. Specifically, we propose to capture spatio-temporal feature
interactions via a convolutional long short-term memory network and model
external factors via a fuzzy neural network that handles data uncertainty
significantly better than deterministic methods. To keep the temporal relations
when fusing two networks and emphasize discriminative spatio-temporal feature
interactions, we employ a novel feature fusion method with a convolution
operation and an attention layer. As far as we know, our work is the first to
fuse a deep recurrent neural network and a fuzzy neural network to model
complex spatial-temporal feature interactions with additional uncertain input
features for predictive learning. Experiments on a large-scale real-world
dataset show that our model achieves more than 10% improvement over the
state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1
Non-commutative geometry of 4-dimensional quantum Hall droplet
We develop the description of non-commutative geometry of the 4-dimensional
quantum Hall fluid's theory proposed recently by Zhang and Hu. The
non-commutative structure of fuzzy appears naturally in this theory.
The fuzzy monopole harmonics, which are the essential elements in this
non-commutative geometry, are explicitly constructed and their obeying the
matrix algebra is obtained. This matrix algebra is associative. We also propose
a fusion scheme of the fuzzy monopole harmonics of the coupling system from
those of the subsystems, and determine the fusion rule in such fusion scheme.
By products, we provide some essential ingredients of the theory of SO(5)
angular momentum. In particular, the explicit expression of the coupling
coefficients, in the theory of SO(5) angular momentum, are given. It is
discussed that some possible applications of our results to the 4-dimensional
quantum Hall system and the matrix brane construction in M-theory.Comment: latex 22 pages, no figures. some references added. some results are
clarifie
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks
Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624
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