1,466 research outputs found
Experiment of Diffuse Reflection Laser Ranging to Space Debris and Data Analysis
Space debris has been posing a serious threat to human space activities and
is needed to be measured and cataloged. As a new technology of space target
surveillance, the measurement accuracy of DRLR (Diffuse Reflection Laser
Ranging) is much higher than that of microwave radar and electro-optical
measurement. Based on laser ranging data of space debris from DRLR system
collected at SHAO (Shanghai Astronomical Observatory) in March-April 2013, the
characteristics and precision of the laser ranging data are analyzed and its
applications in OD (Orbit Determination) of space debris are discussed in this
paper, which is implemented for the first time in China. The experiment
indicates that the precision of laser ranging data can reach 39cm-228cm. When
the data is sufficient enough (4 arcs of 3 days), the orbit accuracy of space
debris can be up to 50m.Comment: 11 pages, 8 figure
Learning Active Basis Models by EM-Type Algorithms
EM algorithm is a convenient tool for maximum likelihood model fitting when
the data are incomplete or when there are latent variables or hidden states. In
this review article we explain that EM algorithm is a natural computational
scheme for learning image templates of object categories where the learning is
not fully supervised. We represent an image template by an active basis model,
which is a linear composition of a selected set of localized, elongated and
oriented wavelet elements that are allowed to slightly perturb their locations
and orientations to account for the deformations of object shapes. The model
can be easily learned when the objects in the training images are of the same
pose, and appear at the same location and scale. This is often called
supervised learning. In the situation where the objects may appear at different
unknown locations, orientations and scales in the training images, we have to
incorporate the unknown locations, orientations and scales as latent variables
into the image generation process, and learn the template by EM-type
algorithms. The E-step imputes the unknown locations, orientations and scales
based on the currently learned template. This step can be considered
self-supervision, which involves using the current template to recognize the
objects in the training images. The M-step then relearns the template based on
the imputed locations, orientations and scales, and this is essentially the
same as supervised learning. So the EM learning process iterates between
recognition and supervised learning. We illustrate this scheme by several
experiments.Comment: Published in at http://dx.doi.org/10.1214/09-STS281 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction
Relation prediction is a task designed for knowledge graph completion which
aims to predict missing relationships between entities. Recent subgraph-based
models for inductive relation prediction have received increasing attention,
which can predict relation for unseen entities based on the extracted subgraph
surrounding the candidate triplet. However, they are not completely inductive
because of their disability of predicting unseen relations. Moreover, they fail
to pay sufficient attention to the role of relation as they only depend on the
model to learn parameterized relation embedding, which leads to inaccurate
prediction on long-tail relations. In this paper, we introduce
Relation-dependent Contrastive Learning (ReCoLe) for inductive relation
prediction, which adapts contrastive learning with a novel sampling method
based on clustering algorithm to enhance the role of relation and improve the
generalization ability to unseen relations. Instead of directly learning
embedding for relations, ReCoLe allocates a pre-trained GNN-based encoder to
each relation to strengthen the influence of relation. The GNN-based encoder is
optimized by contrastive learning, which ensures satisfactory performance on
long-tail relations. In addition, the cluster sampling method equips ReCoLe
with the ability to handle both unseen relations and entities. Experimental
results suggest that ReCoLe outperforms state-of-the-art methods on commonly
used inductive datasets
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