10,856 research outputs found
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition
Advancements in convolutional neural networks (CNNs) have made significant
strides toward achieving high performance levels on multiple object recognition
tasks. While some approaches utilize information from the entire scene to
propose regions of interest, the task of interpreting a particular region or
object is still performed independently of other objects and features in the
image. Here we demonstrate that a scene's 'gist' can significantly contribute
to how well humans can recognize objects. These findings are consistent with
the notion that humans foveate on an object and incorporate information from
the periphery to aid in recognition. We use a biologically inspired two-part
convolutional neural network ('GistNet') that models the fovea and periphery to
provide a proof-of-principle demonstration that computational object
recognition can significantly benefit from the gist of the scene as contextual
information. Our model yields accuracy improvements of up to 50% in certain
object categories when incorporating contextual gist, while only increasing the
original model size by 5%. This proposed model mirrors our intuition about how
the human visual system recognizes objects, suggesting specific biologically
plausible constraints to improve machine vision and building initial steps
towards the challenge of scene understanding
The Incidence Chromatic Number of Toroidal Grids
An incidence in a graph is a pair with and , such that and are incident. Two incidences and
are adjacent if , or , or the edge equals or . The
incidence chromatic number of is the smallest for which there exists a
mapping from the set of incidences of to a set of colors that assigns
distinct colors to adjacent incidences. In this paper, we prove that the
incidence chromatic number of the toroidal grid equals 5
when and 6 otherwise.Comment: 16 page
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Deep learning approaches to breast cancer detection in mammograms have
recently shown promising results. However, such models are constrained by the
limited size of publicly available mammography datasets, in large part due to
privacy concerns and the high cost of generating expert annotations. Limited
dataset size is further exacerbated by substantial class imbalance since
"normal" images dramatically outnumber those with findings. Given the rapid
progress of generative models in synthesizing realistic images, and the known
effectiveness of simple data augmentation techniques (e.g. horizontal
flipping), we ask if it is possible to synthetically augment mammogram datasets
using generative adversarial networks (GANs). We train a class-conditional GAN
to perform contextual in-filling, which we then use to synthesize lesions onto
healthy screening mammograms. First, we show that GANs are capable of
generating high-resolution synthetic mammogram patches. Next, we experimentally
evaluate using the augmented dataset to improve breast cancer classification
performance. We observe that a ResNet-50 classifier trained with GAN-augmented
training data produces a higher AUROC compared to the same model trained only
on traditionally augmented data, demonstrating the potential of our approach.Comment: To appear in MICCAI 2018, Breast Image Analysis Worksho
Toda-like (0,2) mirrors to products of projective spaces
One of the open problems in understanding (0,2) mirror symmetry concerns the
construction of Toda-like Landau-Ginzburg mirrors to (0,2) theories on Fano
spaces. In this paper, we begin to fill this gap by making an ansatz for (0,2)
Toda-like theories mirror to (0,2) supersymmetric nonlinear sigma models on
products of projective spaces, with deformations of the tangent bundle,
generalizing a special case previously worked out for P1xP1. We check this
ansatz by matching correlation functions of the B/2-twisted Toda-like theories
to correlation functions of corresponding A/2-twisted nonlinear sigma models,
computed primarily using localization techniques. These (0,2) Landau-Ginzburg
models admit redundancies, which can lend themselves to multiple
distinct-looking representatives of the same physics, which we discuss.Comment: 35 pages, LaTeX; v2: typos fixed; v3: more typos fixe
A Statistical Reconstruction of the Planet Population Around Kepler Solar-Type Stars
Using the cumulative catalog of planets detected by the NASA Kepler mission,
we reconstruct the intrinsic occurrence of Earth- to Neptune-size (1 -
4) planets and their distributions with radius and orbital period.
We analyze 76,711 solar-type () stars with 430 planets
on 20-200~d orbits, excluding close-in planets that may have been affected by
the proximity to the host star. Our analysis considers errors in planet radii
and includes an "iterative simulation" technique that does not bin the data. We
find a radius distribution that peaks at 2-2.8 Earth radii, with lower numbers
of smaller and larger planets. These planets are uniformly distributed with
logarithmic period, and the mean number of such planets per star is . The occurrence is if planets interior to 20~d are included.
We estimate the occurrence of Earth-size planets in the "habitable zone"
(defined as 1-2, 0.99-1.7 AU for solar-like stars) as
. Our results largely agree with those of Petigura et al.
(2013), although we find a higher occurrence of 2.8-4 Earth-radii planets. The
reasons for this excess are the inclusion of errors in planet radius, updated
Huber et al. (2014) stellar parameters, and also the exclusion of planets which
may have been affected by proximity to the host star.Comment: 13 pages, 9 figures, 3 Tables. Accepted for publication in Ap
Many-Server Asymptotics for Join-the-Shortest-Queue: Large Deviations and Rare Events
The Join-the-Shortest-Queue routing policy is studied in an asymptotic regime
where the number of processors scales with the arrival rate. A large
deviation principle (LDP) for the occupancy process is established, as , in a suitable infinite-dimensional path space. Model features that
present technical challenges include, Markovian dynamics with discontinuous
statistics, a diminishing rate property of the transition probability rates,
and an infinite-dimensional state space. The difficulty is in the proof of the
Laplace lower bound which requires establishing the uniqueness of solutions of
certain infinite-dimensional systems of controlled ordinary differential
equations. The LDP gives information on the rate of decay of probabilities of
various types of rare events associated with the system. We illustrate this by
establishing explicit exponential decay rates for probabilities of long queues.
In particular, denoting by the event that there is at least one
queue with or more jobs at some time instant over , we show that, in
the critical case, for large and , Comment: 48 pages, 2 figure
Collapse of a Scalar Field in 2+1 Gravity
We consider the problem of critical gravitational collapse of a scalar field
in 2+1 dimensions with spherical (circular) symmetry. After surveying all the
analytic, continuously self-similar solutions and considering their global
structure, we examine their perturbations with the intent of understanding
which are the critical solutions with a single unstable mode. The critical
solution which we find is the one which agrees most closely with that found in
numerical evolutions. However, the critical exponent which we find does not
seem to agree with the numerical result
Solving Multistage Influence Diagrams using Branch-and-Bound Search
A branch-and-bound approach to solving influ- ence diagrams has been
previously proposed in the literature, but appears to have never been
implemented and evaluated - apparently due to the difficulties of computing
effective bounds for the branch-and-bound search. In this paper, we describe
how to efficiently compute effective bounds, and we develop a practical
implementa- tion of depth-first branch-and-bound search for influence diagram
evaluation that outperforms existing methods for solving influence diagrams
with multiple stages.Comment: Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010
The magnetic structure of bixbyite a-Mn2O3: a combined density functional theory DFT+U and neutron diffraction study
First principles density functional theory DFT+U calculations and
experimental neutron diffraction structure analyses were used to determine the
low-temperature crystallographic and magnetic structure of bixbyite Mn2O3. The
energies of various magnetic arrangements, calculated from first principles,
were fit to a cluster-expansion model using a Bayesian method that overcomes a
problem of underfitting caused by the limited number of input magnetic
configurations. The model was used to predict the lowest-energy magnetic
states. Experimental determination of magnetic structure benefited from
optimized sample synthesis, which produced crystallite sizes large enough to
yield a clear splitting of peaks in the neutron powder diffraction patterns,
thereby enabling magnetic-structure refinements under the correct orthorhombic
symmetry. The refinements employed group theory to constrain magnetic models.
Computational and experimental analyses independently converged to similar
ground states, with identical antiferromagnetic ordering along a principal
magnetic axis and secondary ordering along a single orthogonal axis, differing
only by a phase factor in the modulation patterns. The lowest-energy magnetic
states are compromise solutions to frustrated antiferromagnetic interactions
between certain corner-sharing MnO6 octahedra
What evidence does deep learning model use to classify Skin Lesions?
Melanoma is a type of skin cancer with the most rapidly increasing incidence.
Early detection of melanoma using dermoscopy images significantly increases
patients' survival rate. However, accurately classifying skin lesions by eye,
especially in the early stage of melanoma, is extremely challenging for the
dermatologists. Hence, the discovery of reliable biomarkers will be meaningful
for melanoma diagnosis. Recent years, the value of deep learning empowered
computer-assisted diagnose has been shown in biomedical imaging based decision
making. However, much research focuses on improving disease detection accuracy
but not exploring the evidence of pathology. In this paper, we propose a method
to interpret the deep learning classification findings. Firstly, we propose an
accurate neural network architecture to classify skin lesions. Secondly, we
utilize a prediction difference analysis method that examines each patch on the
image through patch-wised corrupting to detect the biomarkers. Lastly, we
validate that our biomarker findings are corresponding to the patterns in the
literature. The findings can be significant and useful to guide clinical
diagnosis.Comment: 5 page
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