7,772 research outputs found
From brain to earth and climate systems: Small-world interaction networks or not?
We consider recent reports on small-world topologies of interaction networks
derived from the dynamics of spatially extended systems that are investigated
in diverse scientific fields such as neurosciences, geophysics, or meteorology.
With numerical simulations that mimic typical experimental situations we have
identified an important constraint when characterizing such networks:
indications of a small-world topology can be expected solely due to the spatial
sampling of the system along with commonly used time series analysis based
approaches to network characterization
Discriminating different classes of biological networks by analyzing the graphs spectra distribution
The brain's structural and functional systems, protein-protein interaction,
and gene networks are examples of biological systems that share some features
of complex networks, such as highly connected nodes, modularity, and
small-world topology. Recent studies indicate that some pathologies present
topological network alterations relative to norms seen in the general
population. Therefore, methods to discriminate the processes that generate the
different classes of networks (e.g., normal and disease) might be crucial for
the diagnosis, prognosis, and treatment of the disease. It is known that
several topological properties of a network (graph) can be described by the
distribution of the spectrum of its adjacency matrix. Moreover, large networks
generated by the same random process have the same spectrum distribution,
allowing us to use it as a "fingerprint". Based on this relationship, we
introduce and propose the entropy of a graph spectrum to measure the
"uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon
divergences between graph spectra to compare networks. We also introduce
general methods for model selection and network model parameter estimation, as
well as a statistical procedure to test the nullity of divergence between two
classes of complex networks. Finally, we demonstrate the usefulness of the
proposed methods by applying them on (1) protein-protein interaction networks
of different species and (2) on networks derived from children diagnosed with
Attention Deficit Hyperactivity Disorder (ADHD) and typically developing
children. We conclude that scale-free networks best describe all the
protein-protein interactions. Also, we show that our proposed measures
succeeded in the identification of topological changes in the network while
other commonly used measures (number of edges, clustering coefficient, average
path length) failed
Resource-constrained classification using a cascade of neural network layers
Deep neural networks are the state of the art technique for a wide variety of classification problems. Although deeper networks are able to make more accurate classifications, the value brought by an additional hidden layer diminishes rapidly. Even shallow networks are able to achieve relatively good results on various classification problems. Only for a small subset of the samples do the deeper layers make a significant difference. We describe an architecture in which only the samples that can not be classified with a sufficient confidence by a shallow network have to be processed by the deeper layers. Instead of training a network with one output layer at the end of the network, we train several output layers, one for each hidden layer. When an output layer is sufficiently confident in this result, we stop propagating at this layer and the deeper layers need not be evaluated. The choice of a threshold confidence value allows us to trade-off accuracy and speed.
Applied in the Internet-of-things (IoT) context, this approach makes it possible to distribute the layers of a neural network between low powered devices and powerful servers in the cloud. We only need the remote layers when the local layers are unable to make an accurate classification. Such an architecture adds the intelligence of a deep neural network to resource constrained devices such as sensor nodes and various IoT devices.
We evaluated our approach on the MNIST and CIFAR10 datasets. On the MNIST dataset, we retain the same accuracy at half the computational cost. On the more difficult CIFAR10 dataset we were able to obtain a relative speed-up of 33% at an marginal increase in error rate from 15.3% to 15.8%
Reconstructing directed and weighted topologies of phase-locked oscillator networks
The formalism of complex networks is extensively employed to describe the
dynamics of interacting agents in several applications. The features of the
connections among the nodes in a network are not always provided beforehand,
hence the problem of appropriately inferring them often arises. Here, we
present a method to reconstruct directed and weighted topologies (REDRAW) of
networks of heterogeneous phase-locked nonlinear oscillators. We ultimately
plan on using REDRAW to infer the interaction structure in human ensembles
engaged in coordination tasks, and give insights into the overall behavior
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the
semantic and clinical efficacy of automatic segmentation methods relevant for
monitoring osteoarthritis progression.
Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at
two timepoints with ground-truth articular (femoral, tibial, patellar)
cartilage and meniscus segmentations was standardized. Challenge submissions
and a majority-vote ensemble were evaluated using Dice score, average symmetric
surface distance, volumetric overlap error, and coefficient of variation on a
hold-out test set. Similarities in network segmentations were evaluated using
pairwise Dice correlations. Articular cartilage thickness was computed per-scan
and longitudinally. Correlation between thickness error and segmentation
metrics was measured using Pearson's coefficient. Two empirical upper bounds
for ensemble performance were computed using combinations of model outputs that
consolidated true positives and true negatives.
Results: Six teams (T1-T6) submitted entries for the challenge. No
significant differences were observed across all segmentation metrics for all
tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice
correlations between network pairs were high (>0.85). Per-scan thickness errors
were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal
bias (<0.03mm). Low correlations (<0.41) were observed between segmentation
metrics and thickness error. The majority-vote ensemble was comparable to top
performing networks (p=1.0). Empirical upper bound performances were similar
for both combinations (p=1.0).
Conclusion: Diverse networks learned to segment the knee similarly where high
segmentation accuracy did not correlate to cartilage thickness accuracy. Voting
ensembles did not outperform individual networks but may help regularize
individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo
- …