10,394 research outputs found
Learning Dictionaries with Bounded Self-Coherence
Sparse coding in learned dictionaries has been established as a successful
approach for signal denoising, source separation and solving inverse problems
in general. A dictionary learning method adapts an initial dictionary to a
particular signal class by iteratively computing an approximate factorization
of a training data matrix into a dictionary and a sparse coding matrix. The
learned dictionary is characterized by two properties: the coherence of the
dictionary to observations of the signal class, and the self-coherence of the
dictionary atoms. A high coherence to the signal class enables the sparse
coding of signal observations with a small approximation error, while a low
self-coherence of the atoms guarantees atom recovery and a more rapid residual
error decay rate for the sparse coding algorithm. The two goals of high signal
coherence and low self-coherence are typically in conflict, therefore one seeks
a trade-off between them, depending on the application. We present a dictionary
learning method with an effective control over the self-coherence of the
trained dictionary, enabling a trade-off between maximizing the sparsity of
codings and approximating an equiangular tight frame.Comment: 4 pages, 2 figures; IEEE Signal Processing Letters, vol. 19, no. 12,
201
Human Capital Investments in Children: A Comparative Analysis of the Role of Parent-Child Shared Time in Selected Countries
Parents invest in their children's human capital in several ways. We investigate the extent to which the levels and composition of parent-child time varies across countries with different welfare regimes: Finland, Germany and the United States. We test the hypothesis of parent-child time as a form of human capital investment in children using a propensity score treatment effects approach that accounts for the possible endogenous nature of time use and human capital investment. Result: There is considerable evidence of welfare regime effects on parent-child shared time. Our results provide mixed support for the hypothesis that non-care related parent-child time is human capital enriching. The strongest support is found in the case of leisure time and eating time.parent-child time, comparative research, welfare regimes, Finland, Germany, USA, treatment effects, propensity score matching
Cu nuclear magnetic resonance study of charge and spin stripe order in LaBaCuO
We present a Cu nuclear magnetic/quadrupole resonance study of the charge
stripe ordered phase of LBCO, with detection of previously unobserved
('wiped-out') signal. We show that spin-spin and spin-lattice relaxation rates
are strongly enhanced in the charge ordered phase, explaining the apparent
signal decrease in earlier investigations. The enhancement is caused by
magnetic, rather than charge fluctuations, conclusively confirming the
long-suspected assumption that spin fluctuations are responsible for the
wipeout effect. Observation of the full Cu signal enables insight into the spin
and charge dynamics of the stripe-ordered phase, and measurements in external
magnetic fields provide information on the nature and suppression of spin
fluctuations associated with charge order. We find glassy spin dynamics, in
agreement with previous work, and incommensurate static charge order with
charge modulation amplitude similar to other cuprate compounds, suggesting that
the amplitude of charge stripes is universal in the cuprates.Comment: 7 pages, 5 figure
Molecular mechanisms of autoimmunity triggered by microbial infection
Autoimmunity can be triggered by microbial infection. In this context, the discovery of Toll-like receptors (TLRs) provides new insights and research perspectives. TLRs induce innate and adaptive antimicrobial immune responses upon exposure to common pathogen-associated molecules, including lipopeptides, lipopolysaccharides, and nucleic acids. They also have the potential, however, to trigger autoimmune disease, as has been revealed by an increasing number of experimental reports. This review summarizes important facts about TLR biology, available data on their role in autoimmunity, and potential consequences for the management of patients with autoimmune disease
Fusing Continuous-valued Medical Labels using a Bayesian Model
With the rapid increase in volume of time series medical data available
through wearable devices, there is a need to employ automated algorithms to
label data. Examples of labels include interventions, changes in activity (e.g.
sleep) and changes in physiology (e.g. arrhythmias). However, automated
algorithms tend to be unreliable resulting in lower quality care. Expert
annotations are scarce, expensive, and prone to significant inter- and
intra-observer variance. To address these problems, a Bayesian
Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable
estimation of label aggregation while accurately infer the precision and bias
of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic
indicator) estimation from the electrocardiogram using labels from the 2006
PhysioNet/Computing in Cardiology Challenge database. It was compared to the
mean, median, and a previously proposed Expectation Maximization (EM) label
aggregation approaches. While accurately predicting each labelling algorithm's
bias and precision, the root-mean-square error of the BCLA was
11.780.63ms, significantly outperforming the best Challenge entry
(15.372.13ms) as well as the EM, mean, and median voting strategies
(14.760.52ms, 17.610.55ms, and 14.430.57ms respectively with
)
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