1,025 research outputs found
Working With CUORE In Search for The Neutrinoless-Double Beta Decay
The neutrino, if found to be its own anti-particle, will reshape the Standard Model of physics. This paper will give some background information regarding CUORE’s experiment to discover the radioactive process known as neutrinoless double-beta decay, how their experiment works, and my own involvement in their research during the installation phase of the project in the summer of 2017
Buckling analysis of piezoelectric composite plates using NURBS-based isogeometric finite elements and higher-order shear deformation theory
A cell-based smoothed discrete shear gap method (CS-DSG3) using triangular elements for static and free vibration analyses of shell structures
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
We present a Bayesian nonparametric framework for multilevel clustering which
utilizes group-level context information to simultaneously discover
low-dimensional structures of the group contents and partitions groups into
clusters. Using the Dirichlet process as the building block, our model
constructs a product base-measure with a nested structure to accommodate
content and context observations at multiple levels. The proposed model
possesses properties that link the nested Dirichlet processes (nDP) and the
Dirichlet process mixture models (DPM) in an interesting way: integrating out
all contents results in the DPM over contexts, whereas integrating out
group-specific contexts results in the nDP mixture over content variables. We
provide a Polya-urn view of the model and an efficient collapsed Gibbs
inference procedure. Extensive experiments on real-world datasets demonstrate
the advantage of utilizing context information via our model in both text and
image domains.Comment: Full version of ICML 201
Automatically learning structural units in educational videos with the hierarchical hidden Markov models
In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations
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