4,384 research outputs found
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas
The availability of large scale databases containing imaging and non-imaging
data, such as the UK Biobank, represents an opportunity to improve our
understanding of healthy and diseased bodily function. Cardiac motion atlases
provide a space of reference in which the motion fields of a cohort of subjects
can be directly compared. In this work, a cardiac motion atlas is built from
cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality
control strategies are proposed to reject subjects with insufficient image
quality. Based on the atlas, three dimensionality reduction algorithms are
evaluated to learn data-driven cardiac motion descriptors, and statistical
methods used to study the association between these descriptors and non-imaging
data. Results show a positive correlation between the atlas motion descriptors
and body fat percentage, basal metabolic rate, hypertension, smoking status and
alcohol intake frequency. The proposed method outperforms the ability to
identify changes in cardiac function due to these known cardiovascular risk
factors compared to ejection fraction, the most commonly used descriptor of
cardiac function. In conclusion, this work represents a framework for further
investigation of the factors influencing cardiac health.Comment: 2018 International Workshop on Statistical Atlases and Computational
Modeling of the Hear
Spatial competition and price formation
We look at price formation in a retail setting, that is, companies set
prices, and consumers either accept prices or go someplace else. In contrast to
most other models in this context, we use a two-dimensional spatial structure
for information transmission, that is, consumers can only learn from nearest
neighbors. Many aspects of this can be understood in terms of generalized
evolutionary dynamics. In consequence, we first look at spatial competition and
cluster formation without price. This leads to establishement size
distributions, which we compare to reality. After some theoretical
considerations, which at least heuristically explain our simulation results, we
finally return to price formation, where we demonstrate that our simple model
with nearly no organized planning or rationality on the part of any of the
agents indeed leads to an economically plausible price.Comment: Minor change
Principal Component Analysis with Noisy and/or Missing Data
We present a method for performing Principal Component Analysis (PCA) on
noisy datasets with missing values. Estimates of the measurement error are used
to weight the input data such that compared to classic PCA, the resulting
eigenvectors are more sensitive to the true underlying signal variations rather
than being pulled by heteroskedastic measurement noise. Missing data is simply
the limiting case of weight=0. The underlying algorithm is a noise weighted
Expectation Maximization (EM) PCA, which has additional benefits of
implementation speed and flexibility for smoothing eigenvectors to reduce the
noise contribution. We present applications of this method on simulated data
and QSO spectra from the Sloan Digital Sky Survey.Comment: Accepted for publication in PASP; v2 with minor updates, mostly to
bibliograph
Progressive managerial bonuses in a spatial Bertrand duopoly
The relationship of managerial bonuses and profit maximization is interesting both from an economic and a managerial viewpoint. Our contribution to this literature is showing that progressive managerial bonuses can increase profits in a spatial Bertrand competition, and furthermore they can help collusion
Analysis of ocean wave characteristic in Western Indonesian Seas using wave spectrum model
Understanding the characteristics of the ocean wave in Indonesian Seas particularly in western Indonesian Seas is crucial to establish secured marine activities in addition to construct well-built marine infrastructures. Three-years-data (July 1996 - 1999) simulated from Simulating Waves Nearshore (SWAN) model were used to analyze the ocean wave characteristics and variabilities in eastern Indian Ocean, Java Sea, and South China Sea. The interannual or seasonal variability of the significant wave height is affected by the alteration of wind speed and direction. Interactions between Indian Ocean Dipole Mode (IODM), El Niño Southern Oscillation (ENSO) and monsoon result in interannual ocean wave variability in the study areas. Empirical Orthogonal Functions (EOF) analysis produces 6 modes represents 95% of total variance that influence the wave height variability in the entire model domain. Mode 1 was dominated by annual monsoon and has spatial dominant contribution in South China Sea effected by ENSO and Indian Ocean influenced by IODM. Java Sea was influenced by Mode 2 which is controlled by semi-annual monsoon and IODM. A positive (negative) IODM strengthens (weakens) the winds speed in Java Sea during the East (West) season and hence contributes to Mode 2 in increasing (decreasing) the significant wave in Java Sea
An Economic Study of the Effect of Android Platform Fragmentation on Security Updates
Vendors in the Android ecosystem typically customize their devices by
modifying Android Open Source Project (AOSP) code, adding in-house developed
proprietary software, and pre-installing third-party applications. However,
research has documented how various security problems are associated with this
customization process.
We develop a model of the Android ecosystem utilizing the concepts of game
theory and product differentiation to capture the competition involving two
vendors customizing the AOSP platform. We show how the vendors are incentivized
to differentiate their products from AOSP and from each other, and how prices
are shaped through this differentiation process. We also consider two types of
consumers: security-conscious consumers who understand and care about security,
and na\"ive consumers who lack the ability to correctly evaluate security
properties of vendor-supplied Android products or simply ignore security. It is
evident that vendors shirk on security investments in the latter case.
Regulators such as the U.S. Federal Trade Commission have sanctioned Android
vendors for underinvestment in security, but the exact effects of these
sanctions are difficult to disentangle with empirical data. Here, we model the
impact of a regulator-imposed fine that incentivizes vendors to match a minimum
security standard. Interestingly, we show how product prices will decrease for
the same cost of customization in the presence of a fine, or a higher level of
regulator-imposed minimum security.Comment: 22nd International Conference on Financial Cryptography and Data
Security (FC 2018
ADVISE: Symbolism and External Knowledge for Decoding Advertisements
In order to convey the most content in their limited space, advertisements
embed references to outside knowledge via symbolism. For example, a motorcycle
stands for adventure (a positive property the ad wants associated with the
product being sold), and a gun stands for danger (a negative property to
dissuade viewers from undesirable behaviors). We show how to use symbolic
references to better understand the meaning of an ad. We further show how
anchoring ad understanding in general-purpose object recognition and image
captioning improves results. We formulate the ad understanding task as matching
the ad image to human-generated statements that describe the action that the ad
prompts, and the rationale it provides for taking this action. Our proposed
method outperforms the state of the art on this task, and on an alternative
formulation of question-answering on ads. We show additional applications of
our learned representations for matching ads to slogans, and clustering ads
according to their topic, without extra training.Comment: To appear, Proceedings of the European Conference on Computer Vision
(ECCV
Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
The joint analysis of biomedical data in Alzheimer's Disease (AD) is
important for better clinical diagnosis and to understand the relationship
between biomarkers. However, jointly accounting for heterogeneous measures
poses important challenges related to the modeling of the variability and the
interpretability of the results. These issues are here addressed by proposing a
novel multi-channel stochastic generative model. We assume that a latent
variable generates the data observed through different channels (e.g., clinical
scores, imaging, ...) and describe an efficient way to estimate jointly the
distribution of both latent variable and data generative process. Experiments
on synthetic data show that the multi-channel formulation allows superior data
reconstruction as opposed to the single channel one. Moreover, the derived
lower bound of the model evidence represents a promising model selection
criterion. Experiments on AD data show that the model parameters can be used
for unsupervised patient stratification and for the joint interpretation of the
heterogeneous observations. Because of its general and flexible formulation, we
believe that the proposed method can find important applications as a general
data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with
MICCAI 2018, September 20, Granada, Spai
Topology of energy surfaces and existence of transversal Poincar\'e sections
Two questions on the topology of compact energy surfaces of natural two
degrees of freedom Hamiltonian systems in a magnetic field are discussed. We
show that the topology of this 3-manifold (if it is not a unit tangent bundle)
is uniquely determined by the Euler characteristic of the accessible region in
configuration space. In this class of 3-manifolds for most cases there does not
exist a transverse and complete Poincar\'e section. We show that there are
topological obstacles for its existence such that only in the cases of
and such a Poincar\'e section can exist.Comment: 10 pages, LaTe
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