8,612 research outputs found
Opinion Dynamics and Communication Networks
This paper examines the interplay of opinion exchange dynamics and
communication network formation. An opinion formation procedure is introduced
which is based on an abstract representation of opinions as --dimensional
bit--strings. Individuals interact if the difference in the opinion strings is
below a defined similarity threshold . Depending on , different
behaviour of the population is observed: low values result in a state of highly
fragmented opinions and higher values yield consensus. The first contribution
of this research is to identify the values of parameters and , such
that the transition between fragmented opinions and homogeneity takes place.
Then, we look at this transition from two perspectives: first by studying the
group size distribution and second by analysing the communication network that
is formed by the interactions that take place during the simulation. The
emerging networks are classified by statistical means and we find that
non--trivial social structures emerge from simple rules for individual
communication. Generating networks allows to compare model outcomes with
real--world communication patterns.Comment: 14 pages 6 figure
Impact of Inter-Country Distances on International Tourism
Tourism is a worldwide practice with international tourism revenues
increasing from US\$495 billion in 2000 to US\$1340 billion in 2017. Its
relevance to the economy of many countries is obvious. Even though the World
Airline Network (WAN) is global and has a peculiar construction, the
International Tourism Network (ITN) is very similar to a random network and
barely global in its reach. To understand the impact of global distances on
local flows, we map the flow of tourists around the world onto a complex
network and study its topological and dynamical balance. We find that although
the WAN serves as infrastructural support for the ITN, the flow of tourism does
not correlate strongly with the extent of flight connections worldwide.
Instead, unidirectional flows appear locally forming communities that shed
light on global travelling behaviour inasmuch as there is only a 15%
probability of finding bidirectional tourism between a pair of countries. We
conjecture that this is a consequence of one-way cyclic tourism by analyzing
the triangles that are formed by the network of flows in the ITN. Finally, we
find that most tourists travel to neighbouring countries and mainly cover
larger distances when there is a direct flight, irrespective of the time it
takes
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network
Histology imaging is an essential diagnosis method to finalize the grade and
stage of cancer of different tissues, especially for breast cancer diagnosis.
Specialists often disagree on the final diagnosis on biopsy tissue due to the
complex morphological variety. Although convolutional neural networks (CNN)
have advantages in extracting discriminative features in image classification,
directly training a CNN on high resolution histology images is computationally
infeasible currently. Besides, inconsistent discriminative features often
distribute over the whole histology image, which incurs challenges in
patch-based CNN classification method. In this paper, we propose a novel
architecture for automatic classification of high resolution histology images.
First, an adapted residual network is employed to explore hierarchical features
without attenuation. Second, we develop a robust deep fusion network to utilize
the spatial relationship between patches and learn to correct the prediction
bias generated from inconsistent discriminative feature distribution. The
proposed method is evaluated using 10-fold cross-validation on 400 high
resolution breast histology images with balanced labels and reports 95%
accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class
classification (carcinoma and non-carcinoma), which substantially outperforms
previous methods and close to pathologist performance.Comment: 8 pages, MICCAI workshop preceeding
Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification
Microscopic histology image analysis is a cornerstone in early detection of
breast cancer. However these images are very large and manual analysis is error
prone and very time consuming. Thus automating this process is in high demand.
We proposed a hierarchical system of convolutional neural networks (CNN) that
classifies automatically patches of these images into four pathologies: normal,
benign, in situ carcinoma and invasive carcinoma. We evaluated our system on
the BACH challenge dataset of image-wise classification and a small dataset
that we used to extend it. Using a train/test split of 75%/25%, we achieved an
accuracy rate of 0.99 on the test split for the BACH dataset and 0.96 on that
of the extension. On the test of the BACH challenge, we've reached an accuracy
of 0.81 which rank us to the 8th out of 51 teams
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
While challenging, the dense segmentation of histology images is a necessary
first step to assess changes in tissue architecture and cellular morphology.
Although specific convolutional neural network architectures have been applied
with great success to the problem, few effectively incorporate visual context
information from multiple scales. With this paper, we present a systematic
comparison of different architectures to assess how including multi-scale
information affects segmentation performance. A publicly available breast
cancer and a locally collected prostate cancer datasets are being utilised for
this study. The results support our hypothesis that visual context and scale
play a crucial role in histology image classification problems
Neutron Charge Radius: Relativistic Effects and the Foldy Term
The neutron charge radius is studied within a light-front model with
different spin coupling schemes and wave functions. The cancellation of the
contributions from the Foldy term and Dirac form factor to the neutron charge
form factor is verified for large nucleon sizes and it is independent of the
detailed form of quark spin coupling and wave function. For the physical
nucleon our results for the contribution of the Dirac form factor to the
neutron radius are insensitive to the form of the wave function while they
strongly depend on the quark spin coupling scheme.Comment: 12 pages, 5 figures, Latex, Int. J. Mod. Phys.
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