125,444 research outputs found
TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation
Medical images from different clinics are acquired with different instruments and settings.
To perform segmentation on these images as a cloud-based service we need to train with multiple datasets
to increase the segmentation independency from the source. We also require an ef cient and fast segmentation
network. In this work these two problems, which are essential for many practical medical imaging
applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep
neural networks which have been shown to be effective for medical image segmentation. Many different
U-Net implementations have been proposed.With the recent development of tensor processing units (TPU),
the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud
services. In this paper, we study, using Google's publicly available colab environment, a generalized fully
con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction.
As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to
glaucoma detection. To obtain networks with a good performance, independently of the image acquisition
source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result
of this study, we have developed a set of functions that allow the implementation of generalized U-Nets
adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-
Total cost of operating an information engine
We study a two-level system controlled in a discrete feedback loop, modeling
both the system and the controller in terms of stochastic Markov processes. We
find that the extracted work, which is known to be bounded from above by the
mutual information acquired during measurement, has to be compensated by an
additional energy supply during the measurement process itself, which is
bounded by the same mutual information from below. Our results confirm that the
total cost of operating an information engine is in full agreement with the
conventional second law of thermodynamics. We also consider the efficiency of
the information engine as function of the cycle time and discuss the operating
condition for maximal power generation. Moreover, we find that the entropy
production of our information engine is maximal for maximal efficiency, in
sharp contrast to conventional reversible heat engines.Comment: PDFLaTeX, 12 pages, 11 figure
Modeling Maxwell's demon with a microcanonical Szilard engine
Following recent work by Marathe and Parrondo [PRL, 104, 245704 (2010)], we
construct a classical Hamiltonian system whose energy is reduced during the
adiabatic cycling of external parameters, when initial conditions are sampled
microcanonically. Combining our system with a device that measures its energy,
we propose a cyclic procedure during which energy is extracted from a heat bath
and converted to work, in apparent violation of the second law of
thermodynamics. This paradox is resolved by deriving an explicit relationship
between the average work delivered during one cycle of operation, and the
average information gained when measuring the system's energy
Regression Trees and Random forest based feature selection for malaria risk exposure prediction
This paper deals with prediction of anopheles number, the main vector of
malaria risk, using environmental and climate variables. The variables
selection is based on an automatic machine learning method using regression
trees, and random forests combined with stratified two levels cross validation.
The minimum threshold of variables importance is accessed using the quadratic
distance of variables importance while the optimal subset of selected variables
is used to perform predictions. Finally the results revealed to be
qualitatively better, at the selection, the prediction , and the CPU time point
of view than those obtained by GLM-Lasso method
Generalized Network Psychometrics: Combining Network and Latent Variable Models
We introduce the network model as a formal psychometric model,
conceptualizing the covariance between psychometric indicators as resulting
from pairwise interactions between observable variables in a network structure.
This contrasts with standard psychometric models, in which the covariance
between test items arises from the influence of one or more common latent
variables. Here, we present two generalizations of the network model that
encompass latent variable structures, establishing network modeling as parts of
the more general framework of Structural Equation Modeling (SEM). In the first
generalization, we model the covariance structure of latent variables as a
network. We term this framework Latent Network Modeling (LNM) and show that,
with LNM, a unique structure of conditional independence relationships between
latent variables can be obtained in an explorative manner. In the second
generalization, the residual variance-covariance structure of indicators is
modeled as a network. We term this generalization Residual Network Modeling
(RNM) and show that, within this framework, identifiable models can be obtained
in which local independence is structurally violated. These generalizations
allow for a general modeling framework that can be used to fit, and compare,
SEM models, network models, and the RNM and LNM generalizations. This
methodology has been implemented in the free-to-use software package lvnet,
which contains confirmatory model testing as well as two exploratory search
algorithms: stepwise search algorithms for low-dimensional datasets and
penalized maximum likelihood estimation for larger datasets. We show in
simulation studies that these search algorithms performs adequately in
identifying the structure of the relevant residual or latent networks. We
further demonstrate the utility of these generalizations in an empirical
example on a personality inventory dataset.Comment: Published in Psychometrik
- …