74 research outputs found
Bayesian Statistical Inference on Elliptical Matrix Distributions
In this paper we are concerned with Bayesian statistical inference for a class of elliptical distributions with parameters μ and Σ. Under a noninformative prior distribution, we obtain the posterior distribution, posterior mean, and generalized maximim likelihood estimators of μ and Σ. Under the entropy loss and quadratic loss, the best Bayesian estimators of Σ are derived as well. Some applications are given
A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents
The relation triples extraction method based on table filling can address the
issues of relation overlap and bias propagation. However, most of them only
establish separate table features for each relationship, which ignores the
implicit relationship between different entity pairs and different relationship
features. Therefore, a feature reasoning relational triple extraction method
based on table filling for technological patents is proposed to explore the
integration of entity recognition and entity relationship, and to extract
entity relationship triples from multi-source scientific and technological
patents data. Compared with the previous methods, the method we proposed for
relational triple extraction has the following advantages: 1) The table filling
method that saves more running space enhances the speed and efficiency of the
model. 2) Based on the features of existing token pairs and table relations,
reasoning the implicit relationship features, and improve the accuracy of
triple extraction. On five benchmark datasets, we evaluated the model we
suggested. The result suggest that our model is advanced and effective, and it
performed well on most of these datasets
OccupancyDETR: Making Semantic Scene Completion as Straightforward as Object Detection
Visual-based 3D semantic occupancy perception (also known as 3D semantic
scene completion) is a new perception paradigm for robotic applications like
autonomous driving. Compared with Bird's Eye View (BEV) perception, it extends
the vertical dimension, significantly enhancing the ability of robots to
understand their surroundings. However, due to this very reason, the
computational demand for current 3D semantic occupancy perception methods
generally surpasses that of BEV perception methods and 2D perception methods.
We propose a novel 3D semantic occupancy perception method, OccupancyDETR,
which consists of a DETR-like object detection module and a 3D occupancy
decoder module. The integration of object detection simplifies our method
structurally - instead of predicting the semantics of each voxels, it
identifies objects in the scene and their respective 3D occupancy grids. This
speeds up our method, reduces required resources, and leverages object
detection algorithm, giving our approach notable performance on small objects.
We demonstrate the effectiveness of our proposed method on the SemanticKITTI
dataset, showcasing an mIoU of 23 and a processing speed of 6 frames per
second, thereby presenting a promising solution for real-time 3D semantic scene
completion
Real-time Monitoring for the Next Core-Collapse Supernova in JUNO
Core-collapse supernova (CCSN) is one of the most energetic astrophysical
events in the Universe. The early and prompt detection of neutrinos before
(pre-SN) and during the SN burst is a unique opportunity to realize the
multi-messenger observation of the CCSN events. In this work, we describe the
monitoring concept and present the sensitivity of the system to the pre-SN and
SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is
a 20 kton liquid scintillator detector under construction in South China. The
real-time monitoring system is designed with both the prompt monitors on the
electronic board and online monitors at the data acquisition stage, in order to
ensure both the alert speed and alert coverage of progenitor stars. By assuming
a false alert rate of 1 per year, this monitoring system can be sensitive to
the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos
up to about 370 (360) kpc for a progenitor mass of 30 for the case
of normal (inverted) mass ordering. The pointing ability of the CCSN is
evaluated by using the accumulated event anisotropy of the inverse beta decay
interactions from pre-SN or SN neutrinos, which, along with the early alert,
can play important roles for the followup multi-messenger observations of the
next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure
Bayesian Statistical Inference on Elliptical Matrix Distributions
In this paper we are concerned with Bayesian statistical inference for a class of elliptical distributions with parameters[mu]and[Sigma]. Under a noninformative prior distribution, we obtain the posterior distribution, posterior mean, and generalized maximim likelihood estimators of[mu]and[Sigma]. Under the entropy loss and quadratic loss, the best Bayesian estimators of[Sigma]are derived as well. Some applications are given.elliptical matrix distributions entropy loss posterior mean quadratic loss
An effective algorithm for generation of factorial designs with generalized minimum aberration
Fractional factorial designs are popular and widely used for industrial experiments. Generalized minimum aberration is an important criterion recently proposed for both regular and non-regular designs. This paper provides a formal optimization treatment on optimal designs with generalized minimum aberration. New lower bounds and optimality results are developed for resolution-III designs. Based on these results, an effective computer search algorithm is provided for sub-design selection, and new optimal designs are reported. © 2007 Elsevier Inc. All rights reserved.Link_to_subscribed_fulltex
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