7,880 research outputs found
Modelling Rod-like Flexible Biological Tissues for Medical Training
This paper outlines a framework for the modelling of slender rod-like biological tissue structures in both global and local scales. Volumetric discretization of a rod-like structure is expensive in computation and therefore
is not ideal for applications where real-time performance is essential. In our approach, the Cosserat rod model is introduced to capture the global shape changes, which models the structure as a one-dimensional entity, while the
local deformation is handled separately. In this way a good balance in accuracy and efficiency is achieved. These advantages make our method appropriate for
the modelling of soft tissues for medical training applications
Polarization Entanglement Purification using Spatial Entanglement
Parametric down-conversion can produce photons that are entangled both in
polarization and in space. Here we show how the spatial entanglement can be
used to purify the polarization entanglement using only linear optical
elements. Spatial entanglement as an additional resource leads to a substantial
improvement in entanglement output compared to a previous scheme.
Interestingly, in the present context the thermal character of down-conversion
sources can be turned into an advantage. Our scheme is realizable with current
technology.Comment: 5 pages, 2 figure
LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation
We present LDAExplore, a tool to visualize topic distributions in a given
document corpus that are generated using Topic Modeling methods. Latent
Dirichlet Allocation (LDA) is one of the basic methods that is predominantly
used to generate topics. One of the problems with methods like LDA is that
users who apply them may not understand the topics that are generated. Also,
users may find it difficult to search correlated topics and correlated
documents. LDAExplore, tries to alleviate these problems by visualizing topic
and word distributions generated from the document corpus and allowing the user
to interact with them. The system is designed for users, who have minimal
knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a
pilot study which uses the abstracts of 322 Information Visualization papers,
where every abstract is considered a document. The topics generated are then
explored by users. The results show that users are able to find correlated
documents and group them based on topics that are similar
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