3,442 research outputs found
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Training robust deep learning (DL) systems for medical image classification
or segmentation is challenging due to limited images covering different disease
types and severity. We propose an active learning (AL) framework to select most
informative samples and add to the training data. We use conditional generative
adversarial networks (cGANs) to generate realistic chest xray images with
different disease characteristics by conditioning its generation on a real
image sample. Informative samples to add to the training set are identified
using a Bayesian neural network. Experiments show our proposed AL framework is
able to achieve state of the art performance by using about 35% of the full
dataset, thus saving significant time and effort over conventional methods
Modeling the Field Emission Current Fluctuation in Carbon Nanotube Thin Films
Owing to their distinct properties, carbon nanotubes (CNTs) have emerged as
promising candidate for field emission devices. It has been found
experimentally that the results related to the field emission performance show
variability. The design of an efficient field emitting device requires the
analysis of the variabilities with a systematic and multiphysics based modeling
approach. In this paper, we develop a model of randomly oriented CNTs in a thin
film by coupling the field emission phenomena, the electron-phonon transport
and the mechanics of single isolated CNT. A computational scheme is developed
by which the states of CNTs are updated in time incremental manner. The device
current is calculated by using Fowler-Nordheim equation for field emission to
study the performance at the device scale.Comment: 4 pages, 5 figure
Acceptance Dependence of Fluctuation in Particle Multiplicity
The effect of limiting the acceptance in rapidity on event-by-event
multiplicity fluctuations in nucleus-nucleus collisions has been investigated.
Our analysis shows that the multiplicity fluctuations decrease when the
rapidity acceptance is decreased. We explain this trend by assuming that the
probability distribution of the particles in the smaller acceptance window
follows binomial distribution. Following a simple statistical analysis we
conclude that the event-by-event multiplicity fluctuations for full acceptance
are likely to be larger than those observed in the experiments, since the
experiments usually have detectors with limited acceptance. We discuss the
application of our model to simulated data generated using VENUS, a widely used
event generator in heavy-ion collisions. We also discuss the results from our
calculations in presence of dynamical fluctuations and possible observation of
these in the actual data.Comment: To appear in Int. J. Mod. Phys.
From Type IIA Black Holes to T-dual Type IIB D-Instantons in N=2, D=4 Supergravity
We discuss the T-duality between the solutions of type IIA versus IIB
superstrings compactified on Calabi-Yau threefolds. Within the context of the
N=2, D=4 supergravity effective Lagrangian, the T-duality transformation is
equivalently described by the c-map, which relates the special Kahler moduli
space of the IIA N=2 vector multiplets to the quaternionic moduli space of the
N=2 hyper multiplets on the type IIB side (and vice versa). Hence the
T-duality, or c-map respectively, transforms the IIA black hole solutions,
originating from even dimensional IIA branes, of the special Kahler effective
action, into IIB D-instanton solutions of the IIB quaternionic sigma-model
action, where the D-instantons can be obtained by compactifying odd IIB
D-branes on the internal Calabi-Yau space. We construct via this mapping a
broad class of D-instanton solutions in four dimensions which are determinded
by a set of harmonic functions plus the underlying topological Calabi-Yau data.Comment: LaTeX, 37 pages. Some typos fixed. Final version, to appear in Nucl.
Phys.
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